Maintained by Difan Deng and Marius Lindauer.
The following list considers papers related to neural architecture search. It is by no means complete. If you miss a paper on the list, please let us know.
Please note that although NAS methods steadily improve, the quality of empirical evaluations in this field are still lagging behind compared to other areas in machine learning, AI and optimization. We would therefore like to share some best practices for empirical evaluations of NAS methods, which we believe will facilitate sustained and measurable progress in the field. If you are interested in a teaser, please read our blog post or directly jump to our checklist.
Transformers have gained increasing popularity in different domains. For a comprehensive list of papers focusing on Neural Architecture Search for Transformer-Based spaces, the awesome-transformer-search repo is all you need.
1.
Yu, Jiandong; Li, Tongtong; Shi, Xuerong; Zhao, Ziyang; Chen, Miao; Zhang, Yu; Wang, Junyu; Yao, Zhijun; Fang, Lei; Hu, Bin
ETMO-NAS: An efficient two-step multimodal one-shot NAS for lung nodules classification Journal Article
In: Biomedical Signal Processing and Control, vol. 104, pp. 107479, 2025, ISSN: 1746-8094.
@article{YU2025107479,
title = {ETMO-NAS: An efficient two-step multimodal one-shot NAS for lung nodules classification},
author = {Jiandong Yu and Tongtong Li and Xuerong Shi and Ziyang Zhao and Miao Chen and Yu Zhang and Junyu Wang and Zhijun Yao and Lei Fang and Bin Hu},
url = {https://www.sciencedirect.com/science/article/pii/S1746809424015374},
doi = {https://doi.org/10.1016/j.bspc.2024.107479},
issn = {1746-8094},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Biomedical Signal Processing and Control},
volume = {104},
pages = {107479},
abstract = {Malignant lung nodules are the initial diagnostic manifestation of lung cancer. Accurate predictive classification of malignant from benign lung nodules can improve treatment efficacy and survival rate of lung cancer patients. Since current deep learning-based PET/CT pulmonary nodule-assisted diagnosis models typically rely on network architectures carefully designed by researchers, which require professional expertise and extensive prior knowledge. To combat these challenges, in this paper, we propose an efficient two-step multimodal one-shot NAS (ETMO-NAS) for searching high-performance network architectures for reliable and accurate classification of lung nodules for multimodal PET/CT data. Specifically, the step I focuses on fully training the performance of all candidate architectures in the search space using the sandwich rule and in-place distillation strategy. The step II aims to split the search space into multiple non-overlapping subsupernets by parallel operation edge decomposition strategy and then fine-tune the subsupernets further improve performance. Finally, the performance of ETMO-NAS was validated on a set of real clinical data. The experimental results show that the classification architecture searched by ETMO-NAS achieves the best performance with accuracy, precision, sensitivity, specificity, and F-1 score of 94.23%, 92.10%, 95.83%, 92.86% and 0.9388, respectively. In addition, compared with the classical CNN model and NAS model, ETMO-NAS performs better with the same inputs, but with only 1/33–1/5 of the parameters. This provides substantial evidence for the competitiveness of the model in classification tasks and presents a new approach for automated diagnosis of PET/CT pulmonary nodules. Code and models will be available at: https://github.com/yujiandong0002/ETMO-NAS.},
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Malignant lung nodules are the initial diagnostic manifestation of lung cancer. Accurate predictive classification of malignant from benign lung nodules can improve treatment efficacy and survival rate of lung cancer patients. Since current deep learning-based PET/CT pulmonary nodule-assisted diagnosis models typically rely on network architectures carefully designed by researchers, which require professional expertise and extensive prior knowledge. To combat these challenges, in this paper, we propose an efficient two-step multimodal one-shot NAS (ETMO-NAS) for searching high-performance network architectures for reliable and accurate classification of lung nodules for multimodal PET/CT data. Specifically, the step I focuses on fully training the performance of all candidate architectures in the search space using the sandwich rule and in-place distillation strategy. The step II aims to split the search space into multiple non-overlapping subsupernets by parallel operation edge decomposition strategy and then fine-tune the subsupernets further improve performance. Finally, the performance of ETMO-NAS was validated on a set of real clinical data. The experimental results show that the classification architecture searched by ETMO-NAS achieves the best performance with accuracy, precision, sensitivity, specificity, and F-1 score of 94.23%, 92.10%, 95.83%, 92.86% and 0.9388, respectively. In addition, compared with the classical CNN model and NAS model, ETMO-NAS performs better with the same inputs, but with only 1/33–1/5 of the parameters. This provides substantial evidence for the competitiveness of the model in classification tasks and presents a new approach for automated diagnosis of PET/CT pulmonary nodules. Code and models will be available at: https://github.com/yujiandong0002/ETMO-NAS.
2.
Wang, Weibo; Li, Hua
NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search Journal Article
In: Biomedical Signal Processing and Control, vol. 100, pp. 107022, 2025, ISSN: 1746-8094.
@article{WANG2025107022,
title = {NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search},
author = {Weibo Wang and Hua Li},
url = {https://www.sciencedirect.com/science/article/pii/S1746809424010802},
doi = {https://doi.org/10.1016/j.bspc.2024.107022},
issn = {1746-8094},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Biomedical Signal Processing and Control},
volume = {100},
pages = {107022},
abstract = {In the detection and recognition of lung nodules, pulmonary nodules vary in size and shape and contain many similar tissues and organs around them, leading to the problems of both missed detection and false detection in existing detection algorithms. Designing proprietary detection and recognition networks manually requires substantial professional expertise. This process is time-consuming and labour-intensive and leads to issues like parameter redundancy and improper feature selection. Therefore, this paper proposes a new pulmonary CAD (computer-aided diagnosis) system for pulmonary nodules, NAS FD Lung (Using the NAS approach to search deep FPN and DPN networks), that can automatically learn and generate a deep learning network tailored to pulmonary nodule detection and recognition task requirements. NAS FD Lung aims to use automatic search to generate deep learning networks in the auxiliary diagnosis of pulmonary nodules to replace the manual design of deep learning networks. NAS FD Lung comprises two automatic search networks: BM NAS-FPN (Using NAS methods to search for deep FPN structures with Binary operation and Matrix multiplication fusion methods) network for nodule detection and NAS-A-DPN (Using the NAS approach to search deep DPN networks with attention mechanism) for nodule identification. The proposed technique is tested on the LUNA16 dataset, and the experimental results show that the model is superior to many existing state-of-the-art approaches. The detection accuracy of lung nodules is 98.23%. Regarding the lung nodules classification, the accuracy, specificity, sensitivity, and AUC values achieved 96.32%,97.14%,95.82%, and 98.33%, respectively.},
keywords = {},
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In the detection and recognition of lung nodules, pulmonary nodules vary in size and shape and contain many similar tissues and organs around them, leading to the problems of both missed detection and false detection in existing detection algorithms. Designing proprietary detection and recognition networks manually requires substantial professional expertise. This process is time-consuming and labour-intensive and leads to issues like parameter redundancy and improper feature selection. Therefore, this paper proposes a new pulmonary CAD (computer-aided diagnosis) system for pulmonary nodules, NAS FD Lung (Using the NAS approach to search deep FPN and DPN networks), that can automatically learn and generate a deep learning network tailored to pulmonary nodule detection and recognition task requirements. NAS FD Lung aims to use automatic search to generate deep learning networks in the auxiliary diagnosis of pulmonary nodules to replace the manual design of deep learning networks. NAS FD Lung comprises two automatic search networks: BM NAS-FPN (Using NAS methods to search for deep FPN structures with Binary operation and Matrix multiplication fusion methods) network for nodule detection and NAS-A-DPN (Using the NAS approach to search deep DPN networks with attention mechanism) for nodule identification. The proposed technique is tested on the LUNA16 dataset, and the experimental results show that the model is superior to many existing state-of-the-art approaches. The detection accuracy of lung nodules is 98.23%. Regarding the lung nodules classification, the accuracy, specificity, sensitivity, and AUC values achieved 96.32%,97.14%,95.82%, and 98.33%, respectively.
3.
AL-Sabri, Raeed; Gao, Jianliang; Chen, Jiamin; Oloulade, Babatounde Moctard; Wu, Zhenpeng; Abdullah, Monir; Hu, Xiaohua
M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions Proceedings Article
In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1783-1788, IEEE Computer Society, Los Alamitos, CA, USA, 2024.
@inproceedings{10821927,
title = { M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions },
author = {Raeed AL-Sabri and Jianliang Gao and Jiamin Chen and Babatounde Moctard Oloulade and Zhenpeng Wu and Monir Abdullah and Xiaohua Hu},
url = {https://doi.ieeecomputersociety.org/10.1109/BIBM62325.2024.10821927},
doi = {10.1109/BIBM62325.2024.10821927},
year = {2024},
date = {2024-12-01},
urldate = {2024-12-01},
booktitle = {2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
pages = {1783-1788},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
abstract = {Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework’s superior performance compared to state-of-the-art models.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework’s superior performance compared to state-of-the-art models.
4.
(Ed.)
Medical Neural Architecture Search: Survey and Taxonomy Collection
2024.
@collection{Benmeziane-ijcai24a,
title = {Medical Neural Architecture Search: Survey and Taxonomy},
author = {Hadjer Benmeziane and Imane Hamzaoui and Kaoutar El Maghraoui and Kaoutar El Maghraoui},
url = {https://www.ijcai.org/proceedings/2024/0878.pdf},
year = {2024},
date = {2024-08-03},
urldate = {2024-08-03},
booktitle = {IJCAI 2024},
keywords = {},
pubstate = {published},
tppubtype = {collection}
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5.
Zhang, Jinnian; Chen, Weijie; Joshi, Tanmayee; Uyanik, Meltem; Zhang, Xiaomin; Loh, Po-Ling; Jog, Varun; Bruce, Richard; Garrett, John; McMillan, Alan
RobMedNAS: searching robust neural network architectures for medical image synthesis Journal Article
In: Biomedical Physics & Engineering Express, vol. 10, no. 5, pp. 055029, 2024.
@article{Zhang_2024,
title = {RobMedNAS: searching robust neural network architectures for medical image synthesis},
author = {Jinnian Zhang and Weijie Chen and Tanmayee Joshi and Meltem Uyanik and Xiaomin Zhang and Po-Ling Loh and Varun Jog and Richard Bruce and John Garrett and Alan McMillan},
url = {https://dx.doi.org/10.1088/2057-1976/ad6e87},
doi = {10.1088/2057-1976/ad6e87},
year = {2024},
date = {2024-08-01},
urldate = {2024-08-01},
journal = {Biomedical Physics & Engineering Express},
volume = {10},
number = {5},
pages = {055029},
publisher = {IOP Publishing},
abstract = {Investigating U-Net model robustness in medical image synthesis against adversarial perturbations, this study introduces RobMedNAS, a neural architecture search strategy for identifying resilient U-Net configurations. Through retrospective analysis of synthesized CT from MRI data, employing Dice coefficient and mean absolute error metrics across critical anatomical areas, the study evaluates traditional U-Net models and RobMedNAS-optimized models under adversarial attacks. Findings demonstrate RobMedNAS’s efficacy in enhancing U-Net resilience without compromising on accuracy, proposing a novel pathway for robust medical image processing.},
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Investigating U-Net model robustness in medical image synthesis against adversarial perturbations, this study introduces RobMedNAS, a neural architecture search strategy for identifying resilient U-Net configurations. Through retrospective analysis of synthesized CT from MRI data, employing Dice coefficient and mean absolute error metrics across critical anatomical areas, the study evaluates traditional U-Net models and RobMedNAS-optimized models under adversarial attacks. Findings demonstrate RobMedNAS’s efficacy in enhancing U-Net resilience without compromising on accuracy, proposing a novel pathway for robust medical image processing.
6.
Asiimwe, Arnold; Das, William; Benmeziane, Hadjer; Maghraoui, Kaoutar El
EDGE2024, 2024.
@conference{Asiimwe-edge24a,
title = {EfficientMedSAM: Accelerating Medical Image Segmentation via Neural Architecture Search and Knowledge Distillation},
author = {Arnold Asiimwe and William Das and Hadjer Benmeziane and Kaoutar El Maghraoui},
url = {https://research.ibm.com/publications/efficientmedsam-accelerating-medical-image-segmentation-via-neural-architecture-search-and-knowledge-distillation},
year = {2024},
date = {2024-07-07},
urldate = {2024-07-07},
booktitle = {EDGE2024},
journal = {EDGE 2024},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
7.
Berezsky, O. M.; Liashchynskyi, P. B.
METHOD OF GENERATIVE-ADVERSARIAL NETWORKS SEARCHING ARCHITECTURES FOR BIOMEDICAL IMAGES SYNTHESIS Journal Article
In: Radio Electronics, Computer Science, Control, no. 1, pp. 104, 2024.
@article{Berezsky_Liashchynskyi_2024,
title = {METHOD OF GENERATIVE-ADVERSARIAL NETWORKS SEARCHING ARCHITECTURES FOR BIOMEDICAL IMAGES SYNTHESIS},
author = {O. M. Berezsky and P. B. Liashchynskyi},
url = {http://ric.zntu.edu.ua/article/view/300976},
doi = {10.15588/1607-3274-2024-1-10},
year = {2024},
date = {2024-04-01},
urldate = {2024-04-01},
journal = {Radio Electronics, Computer Science, Control},
number = {1},
pages = {104},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
8.
Rajesh, Chilukamari; Sadam, Ravichandra; Kumar, Sushil
Automated Deep Learning Models for Medical Image Segmentation and Denoising Proceedings Article
In: 2024 17th International Conference on Signal Processing and Communication System (ICSPCS), pp. 1-7, 2024.
@inproceedings{10815837,
title = {Automated Deep Learning Models for Medical Image Segmentation and Denoising},
author = {Chilukamari Rajesh and Ravichandra Sadam and Sushil Kumar},
url = {https://ieeexplore.ieee.org/abstract/document/10815837},
doi = {10.1109/ICSPCS63175.2024.10815837},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 17th International Conference on Signal Processing and Communication System (ICSPCS)},
pages = {1-7},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
9.
Franco-Gaona, Erick; Avila-Garcia, Maria-Susana; Cruz-Aceves, Ivan; Orocio-Garcia, Hiram-Efrain; Escobedo-Gordillo, Andres; Brieva, Jorge
Neural Architecture Search Using Trajectory Metaheuristics to Classify Coronary Stenosis Proceedings Article
In: 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM), pp. 1-4, 2024.
@inproceedings{10783513,
title = {Neural Architecture Search Using Trajectory Metaheuristics to Classify Coronary Stenosis},
author = {Erick Franco-Gaona and Maria-Susana Avila-Garcia and Ivan Cruz-Aceves and Hiram-Efrain Orocio-Garcia and Andres Escobedo-Gordillo and Jorge Brieva},
url = {https://ieeexplore.ieee.org/abstract/document/10783513},
doi = {10.1109/SIPAIM62974.2024.10783513},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM)},
pages = {1-4},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
10.
Kuş, Zeki; Kiraz, Berna; Aydin, Musa; Kiraz, Alper
BioNAS: Neural Architecture Search for Multiple Data Modalities in Biomedical Image Classification Proceedings Article
In: Garcia, Fausto P.; Jamil, Akhtar; Hameed, Alaa Ali; Ortis, Alessandro; Ramirez, Isaac Segovia (Ed.): Recent Trends and Advances in Artificial Intelligence, pp. 539–550, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-70924-1.
@inproceedings{10.1007/978-3-031-70924-1_41,
title = {BioNAS: Neural Architecture Search for Multiple Data Modalities in Biomedical Image Classification},
author = {Zeki Kuş and Berna Kiraz and Musa Aydin and Alper Kiraz},
editor = {Fausto P. Garcia and Akhtar Jamil and Alaa Ali Hameed and Alessandro Ortis and Isaac Segovia Ramirez},
isbn = {978-3-031-70924-1},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Recent Trends and Advances in Artificial Intelligence},
pages = {539–550},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Neural Architecture Search (NAS) for biomedical image classification has the potential to design highly efficient and accurate networks automatically for tasks from different modalities. This paper presents BioNAS, a new NAS approach designed for multi-modal biomedical image classification. Unlike other methods, BioNAS dynamically adjusts the number of stacks, modules, and feature maps in the network to improve both performance and complexity. The proposed approach utilizes an opposition-based differential evolution optimization technique to identify the optimal network structure. We have compared our methods on two public multi-class classification datasets with different data modalities: DermaMNIST and OrganCMNIST. BioNAS outperforms hand-designed networks, automatic machine learning frameworks, and most NAS studies in terms of accuracy (ACC) and area under the curve (AUC) on the OrganCMNIST and DermaMNIST datasets. The proposed networks significantly outperform all other methods on the DermaMNIST dataset, achieving accuracy improvements of up to 4.4 points and AUC improvements of up to 2.6 points, and also surpass other studies by up to 5.4 points in accuracy and 0.6 points in AUC on OrganCMNIST. Moreover, the proposed networks have fewer parameters than hand-designed architectures like ResNet-18 and ResNet-50. The results indicate that BioNAS has the potential to be an effective alternative to hand-designed networks and automatic frameworks, offering a competitive solution in the classification of biomedical images.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Neural Architecture Search (NAS) for biomedical image classification has the potential to design highly efficient and accurate networks automatically for tasks from different modalities. This paper presents BioNAS, a new NAS approach designed for multi-modal biomedical image classification. Unlike other methods, BioNAS dynamically adjusts the number of stacks, modules, and feature maps in the network to improve both performance and complexity. The proposed approach utilizes an opposition-based differential evolution optimization technique to identify the optimal network structure. We have compared our methods on two public multi-class classification datasets with different data modalities: DermaMNIST and OrganCMNIST. BioNAS outperforms hand-designed networks, automatic machine learning frameworks, and most NAS studies in terms of accuracy (ACC) and area under the curve (AUC) on the OrganCMNIST and DermaMNIST datasets. The proposed networks significantly outperform all other methods on the DermaMNIST dataset, achieving accuracy improvements of up to 4.4 points and AUC improvements of up to 2.6 points, and also surpass other studies by up to 5.4 points in accuracy and 0.6 points in AUC on OrganCMNIST. Moreover, the proposed networks have fewer parameters than hand-designed architectures like ResNet-18 and ResNet-50. The results indicate that BioNAS has the potential to be an effective alternative to hand-designed networks and automatic frameworks, offering a competitive solution in the classification of biomedical images.
11.
Ji, Yuanfeng; 纪源丰,
Towards efficient deep learning for medical image analysis Bachelor Thesis
2024.
@bachelorthesis{HKUHUB_10722_350322,
title = {Towards efficient deep learning for medical image analysis},
author = {Yuanfeng Ji and 纪源丰},
url = {http://hdl.handle.net/10722/350322},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {HKU Theses Online (HKUTO)},
abstract = {The advancement of deep learning in medical image analysis has revolutionized the field of medical diagnostics, improving both the accuracy and efficiency of computer- aided diagnostic systems. Despite substantial progress, significant challenges remain, mainly due to the diversity of medical tasks and the scarcity of high-quality annotated data. This thesis addresses these challenges by proposing efficient deep learning meth- ods that improve the development and evaluation of medical imaging models, ensuring their reliability and effectiveness in clinical settings. First, the thesis presents the Ab- dominal Multi-Organ Segmentation (AMOS) dataset, a robust collection of annotated medical images from different demographics and imaging modalities. AMOS utilizes a semi-automated annotation process powered by a pre-trained model, which not only accelerates annotation, but also improves the accuracy and consistency of the data. This approach helps curate a comprehensive benchmark that reflects the real-world complexity and variability of clinical environments, facilitating rigorous testing and evaluation of medical deep learning applications. Then, to address the diversity of imaging tasks, this thesis presents UXNet, a novel application of Neural Architecture Search (NAS) technology designed to adapt neural network architectures specifically for different medical image analysis tasks. UXNet dynamically adapts to the specifics of the input data and output tasks, optimizing model structures to achieve high levels of accuracy and efficiency in different settings. This reduces the reliance on manual tuning and expert knowledge, streamlining the development process of deep learn- ing solutions for medical imaging. Moreover, recognizing the increasing complexity of deep learning models, the thesis introduces AutoBench, an automated tool for the assessment and governance of these models. Leveraging large language models, Au- toBench automates the creation of evaluation standards and conducts comprehensive performance evaluations, facilitating continuous monitoring and adaptation of model performance in medical applications. Finally, I discuss some future work towards in developing efficient and effective deep learning medical applications.},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
The advancement of deep learning in medical image analysis has revolutionized the field of medical diagnostics, improving both the accuracy and efficiency of computer- aided diagnostic systems. Despite substantial progress, significant challenges remain, mainly due to the diversity of medical tasks and the scarcity of high-quality annotated data. This thesis addresses these challenges by proposing efficient deep learning meth- ods that improve the development and evaluation of medical imaging models, ensuring their reliability and effectiveness in clinical settings. First, the thesis presents the Ab- dominal Multi-Organ Segmentation (AMOS) dataset, a robust collection of annotated medical images from different demographics and imaging modalities. AMOS utilizes a semi-automated annotation process powered by a pre-trained model, which not only accelerates annotation, but also improves the accuracy and consistency of the data. This approach helps curate a comprehensive benchmark that reflects the real-world complexity and variability of clinical environments, facilitating rigorous testing and evaluation of medical deep learning applications. Then, to address the diversity of imaging tasks, this thesis presents UXNet, a novel application of Neural Architecture Search (NAS) technology designed to adapt neural network architectures specifically for different medical image analysis tasks. UXNet dynamically adapts to the specifics of the input data and output tasks, optimizing model structures to achieve high levels of accuracy and efficiency in different settings. This reduces the reliance on manual tuning and expert knowledge, streamlining the development process of deep learn- ing solutions for medical imaging. Moreover, recognizing the increasing complexity of deep learning models, the thesis introduces AutoBench, an automated tool for the assessment and governance of these models. Leveraging large language models, Au- toBench automates the creation of evaluation standards and conducts comprehensive performance evaluations, facilitating continuous monitoring and adaptation of model performance in medical applications. Finally, I discuss some future work towards in developing efficient and effective deep learning medical applications.
12.
Chen, Xi; Lv, Jiahuan; Wang, Zeyu; Qin, Genggeng; Zhou, Zhiguo
In: Computers in Biology and Medicine, vol. 183, pp. 109299, 2024, ISSN: 0010-4825.
@article{CHEN2024109299,
title = {Deep-AutoMO: Deep automated multiobjective neural network for trustworthy lesion malignancy diagnosis in the early stage via digital breast tomosynthesis},
author = {Xi Chen and Jiahuan Lv and Zeyu Wang and Genggeng Qin and Zhiguo Zhou},
url = {https://www.sciencedirect.com/science/article/pii/S0010482524013842},
doi = {https://doi.org/10.1016/j.compbiomed.2024.109299},
issn = {0010-4825},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Computers in Biology and Medicine},
volume = {183},
pages = {109299},
abstract = {Breast cancer is the most prevalent cancer in women, and early diagnosis of malignant lesions is crucial for developing treatment plans. Digital breast tomosynthesis (DBT) has emerged as a valuable tool for early breast cancer detection, as it can identify more lesions and improve the early detection rate. Deep learning has shown great potential in medical image-based cancer diagnosis, including DBT. However, deploying these models in clinical practice may be challenging due to concerns about reliability and robustness. In this study, we developed a novel deep automated multiobjective neural network (Deep-AutoMO) to build a trustworthy model and achieve balance, safety and robustness in a unified way. During the training stage, we introduced a multiobjective immune neural architecture search (MINAS) that simultaneously considers sensitivity and specificity as objective functions, aiming to strike a balance between the two. Each neural network in Deep-AutoMO comprises a combination of a ResNet block, a DenseNet block and a pooling layer. We employ Bayesian optimization to optimize the hyperparameters in the MINAS, enhancing the efficiency of the model training process. In the testing stage, evidential reasoning based on entropy (ERE) approach is proposed to build a safe and robust model. The experimental study on DBT images demonstrated that Deep-AutoMO achieves promising performance with a well-balanced trade-off between sensitivity and specificity, outperforming currently available methods. Moreover, the model's safety is ensured through uncertainty estimation, and its robustness is improved, making it a trustworthy tool for breast cancer diagnosis in clinical settings. We have shared the code on GitHub for other researchers to use. The code can be found at https://github.com/ChaoyangZhang-XJTU/Deep-AutoMO.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Breast cancer is the most prevalent cancer in women, and early diagnosis of malignant lesions is crucial for developing treatment plans. Digital breast tomosynthesis (DBT) has emerged as a valuable tool for early breast cancer detection, as it can identify more lesions and improve the early detection rate. Deep learning has shown great potential in medical image-based cancer diagnosis, including DBT. However, deploying these models in clinical practice may be challenging due to concerns about reliability and robustness. In this study, we developed a novel deep automated multiobjective neural network (Deep-AutoMO) to build a trustworthy model and achieve balance, safety and robustness in a unified way. During the training stage, we introduced a multiobjective immune neural architecture search (MINAS) that simultaneously considers sensitivity and specificity as objective functions, aiming to strike a balance between the two. Each neural network in Deep-AutoMO comprises a combination of a ResNet block, a DenseNet block and a pooling layer. We employ Bayesian optimization to optimize the hyperparameters in the MINAS, enhancing the efficiency of the model training process. In the testing stage, evidential reasoning based on entropy (ERE) approach is proposed to build a safe and robust model. The experimental study on DBT images demonstrated that Deep-AutoMO achieves promising performance with a well-balanced trade-off between sensitivity and specificity, outperforming currently available methods. Moreover, the model's safety is ensured through uncertainty estimation, and its robustness is improved, making it a trustworthy tool for breast cancer diagnosis in clinical settings. We have shared the code on GitHub for other researchers to use. The code can be found at https://github.com/ChaoyangZhang-XJTU/Deep-AutoMO.
13.
Park, Eunbin; Lee, Youngjoo
mDARTS: Searching ML-Based ECG Classifiers against Membership Inference Attacks Journal Article
In: IEEE Journal of Biomedical and Health Informatics, pp. 1-11, 2024.
@article{10720208,
title = {mDARTS: Searching ML-Based ECG Classifiers against Membership Inference Attacks},
author = {Eunbin Park and Youngjoo Lee},
url = {https://ieeexplore.ieee.org/abstract/document/10720208},
doi = {10.1109/JBHI.2024.3481505},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {1-11},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
14.
Zhang, Qing; Shao, Dan; Lin, Lin; Gong, Guoliang; Xu, Rui; Kido, Shoji; Cui, HongWei
Feature Separation in Diffuse Lung Disease Image Classification by Using Evolutionary Algorithm-Based NAS Journal Article
In: IEEE Journal of Biomedical and Health Informatics, pp. 1-12, 2024.
@article{10716760,
title = {Feature Separation in Diffuse Lung Disease Image Classification by Using Evolutionary Algorithm-Based NAS},
author = {Qing Zhang and Dan Shao and Lin Lin and Guoliang Gong and Rui Xu and Shoji Kido and HongWei Cui},
url = {https://ieeexplore.ieee.org/document/10716760},
doi = {10.1109/JBHI.2024.3481012},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {1-12},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
15.
Wu, Yu; Fan, Hailong; Ying, Weiqin; Zhou, Zekun; Zheng, Qiaoqiao; Zhang, Jiajian
Federated Neural Architecture Search with Hierarchical Progressive Acceleration for Medical Image Segmentation Proceedings Article
In: Tan, Ying; Shi, Yuhui (Ed.): Advances in Swarm Intelligence, pp. 112–123, Springer Nature Singapore, Singapore, 2024, ISBN: 978-981-97-7184-4.
@inproceedings{10.1007/978-981-97-7184-4_10,
title = {Federated Neural Architecture Search with Hierarchical Progressive Acceleration for Medical Image Segmentation},
author = {Yu Wu and Hailong Fan and Weiqin Ying and Zekun Zhou and Qiaoqiao Zheng and Jiajian Zhang},
editor = {Ying Tan and Yuhui Shi},
url = {https://link.springer.com/chapter/10.1007/978-981-97-7184-4_10},
isbn = {978-981-97-7184-4},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Advances in Swarm Intelligence},
pages = {112–123},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {Deep neural networks for medical image segmentation often require data from multiple medical institutions, but privacy concerns limit data sharing, making federated learning (FL) a viable alternative. However, predefined network architectures in FL are often suboptimal and need extensive manual tuning. Traditional neural architecture search (NAS) methods are unsuitable for FL due to high communication and evaluation costs. This paper presents an evolutionary NAS method (FS-ENAS) for federated medical image segmentation. FS-ENAS utilizes a U-Net++ based supernet with depthwise separable convolution and adaptable skip connections. It introduces a novel multi-stage, hierarchical progressive acceleration strategy tailored for federated neural architecture search to reduce communication and evaluation burdens. Experimental results on retinal blood vessel segmentation tasks show that FS-ENAS efficiently searches for suitable architectures with reduced communication and evaluation costs while protecting privacy.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Deep neural networks for medical image segmentation often require data from multiple medical institutions, but privacy concerns limit data sharing, making federated learning (FL) a viable alternative. However, predefined network architectures in FL are often suboptimal and need extensive manual tuning. Traditional neural architecture search (NAS) methods are unsuitable for FL due to high communication and evaluation costs. This paper presents an evolutionary NAS method (FS-ENAS) for federated medical image segmentation. FS-ENAS utilizes a U-Net++ based supernet with depthwise separable convolution and adaptable skip connections. It introduces a novel multi-stage, hierarchical progressive acceleration strategy tailored for federated neural architecture search to reduce communication and evaluation burdens. Experimental results on retinal blood vessel segmentation tasks show that FS-ENAS efficiently searches for suitable architectures with reduced communication and evaluation costs while protecting privacy.
16.
Domanski, Peter; Ray, Aritra; Lafata, Kyle; Firouzi, Farshad; Chakrabarty, Krishnendu; Pflüger, Dirk
Advancing blood glucose prediction with neural architecture search and deep reinforcement learning for type 1 diabetics Journal Article
In: Biocybernetics and Biomedical Engineering, vol. 44, no. 3, pp. 481-500, 2024, ISSN: 0208-5216.
@article{DOMANSKI2024481,
title = {Advancing blood glucose prediction with neural architecture search and deep reinforcement learning for type 1 diabetics},
author = {Peter Domanski and Aritra Ray and Kyle Lafata and Farshad Firouzi and Krishnendu Chakrabarty and Dirk Pflüger},
url = {https://www.sciencedirect.com/science/article/pii/S0208521624000536},
doi = {https://doi.org/10.1016/j.bbe.2024.07.006},
issn = {0208-5216},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Biocybernetics and Biomedical Engineering},
volume = {44},
number = {3},
pages = {481-500},
abstract = {For individuals with Type-1 diabetes mellitus, accurate prediction of future blood glucose values is crucial to aid its regulation with insulin administration, tailored to the individual’s specific needs. The authors propose a novel approach for the integration of a neural architecture search framework with deep reinforcement learning to autonomously generate and train architectures, optimized for each subject over model size and analytical prediction performance, for the blood glucose prediction task in individuals with Type-1 diabetes. The authors evaluate the proposed approach on the OhioT1DM dataset, which includes blood glucose monitoring records at 5-min intervals over 8 weeks for 12 patients with Type-1 diabetes mellitus. Prior work focused on predicting blood glucose levels in 30 and 45-min prediction horizons, equivalent to 6 and 9 data points, respectively. Compared to the previously achieved best error, the proposed method demonstrates improvements of 18.4 % and 22.5 % on average for mean absolute error in the 30-min and 45-min prediction horizons, respectively, through the proposed deep reinforcement learning framework. Using the deep reinforcement learning framework, the best-case and worst-case analytical performance measured over root mean square error and mean absolute error was obtained for subject ID 570 and subject ID 584, respectively. Models optimized for performance on the prediction task and model size were obtained after implementing neural architecture search in conjunction with deep reinforcement learning on these two extreme cases. The authors demonstrate improvements of 4.8 % using Long Short Term Memory-based architectures and 5.7 % with Gated Recurrent Units-based architectures for patient ID 570 on the analytical prediction performance by integrating neural architecture search with deep reinforcement learning framework. The patient with the lowest performance (ID 584) on the deep reinforcement learning method had an even greater performance boost, with improvements of 10.0 % and 12.6 % observed for the Long Short-Term Memory and Gated Recurrent Units, respectively. The subject-specific optimized models over performance and model size from the neural architecture search in conjunction with deep reinforcement learning had a reduction in model size which ranged from 20 to 150 times compared to the model obtained using only the deep reinforcement learning method. The smaller size, indicating a reduction in model complexity in terms of the number of trainable network parameters, was achieved without a loss in the prediction performance.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
For individuals with Type-1 diabetes mellitus, accurate prediction of future blood glucose values is crucial to aid its regulation with insulin administration, tailored to the individual’s specific needs. The authors propose a novel approach for the integration of a neural architecture search framework with deep reinforcement learning to autonomously generate and train architectures, optimized for each subject over model size and analytical prediction performance, for the blood glucose prediction task in individuals with Type-1 diabetes. The authors evaluate the proposed approach on the OhioT1DM dataset, which includes blood glucose monitoring records at 5-min intervals over 8 weeks for 12 patients with Type-1 diabetes mellitus. Prior work focused on predicting blood glucose levels in 30 and 45-min prediction horizons, equivalent to 6 and 9 data points, respectively. Compared to the previously achieved best error, the proposed method demonstrates improvements of 18.4 % and 22.5 % on average for mean absolute error in the 30-min and 45-min prediction horizons, respectively, through the proposed deep reinforcement learning framework. Using the deep reinforcement learning framework, the best-case and worst-case analytical performance measured over root mean square error and mean absolute error was obtained for subject ID 570 and subject ID 584, respectively. Models optimized for performance on the prediction task and model size were obtained after implementing neural architecture search in conjunction with deep reinforcement learning on these two extreme cases. The authors demonstrate improvements of 4.8 % using Long Short Term Memory-based architectures and 5.7 % with Gated Recurrent Units-based architectures for patient ID 570 on the analytical prediction performance by integrating neural architecture search with deep reinforcement learning framework. The patient with the lowest performance (ID 584) on the deep reinforcement learning method had an even greater performance boost, with improvements of 10.0 % and 12.6 % observed for the Long Short-Term Memory and Gated Recurrent Units, respectively. The subject-specific optimized models over performance and model size from the neural architecture search in conjunction with deep reinforcement learning had a reduction in model size which ranged from 20 to 150 times compared to the model obtained using only the deep reinforcement learning method. The smaller size, indicating a reduction in model complexity in terms of the number of trainable network parameters, was achieved without a loss in the prediction performance.
17.
Chaiyarin, Sinee; Rojbundit, Napassorn; Piyabenjarad, Panichanok; Limpitigranon, Pimpattra; Wisitthipakdeekul, Siraprapa; Nonthasaen, Pawaree; Achararit, Paniti
Neural architecture search for medicine: A survey Journal Article
In: Informatics in Medicine Unlocked, vol. 50, pp. 101565, 2024, ISSN: 2352-9148.
@article{CHAIYARIN2024101565,
title = {Neural architecture search for medicine: A survey},
author = {Sinee Chaiyarin and Napassorn Rojbundit and Panichanok Piyabenjarad and Pimpattra Limpitigranon and Siraprapa Wisitthipakdeekul and Pawaree Nonthasaen and Paniti Achararit},
url = {https://www.sciencedirect.com/science/article/pii/S2352914824001217},
doi = {https://doi.org/10.1016/j.imu.2024.101565},
issn = {2352-9148},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Informatics in Medicine Unlocked},
volume = {50},
pages = {101565},
abstract = {In this article we examined research on using neural architecture search (NAS) in medical applications, prompted by the current shortage of health care professionals relative to patient volumes. We explored the current state of NAS development in various medical fields, evaluated its performance, and examined potential future directions of NAS in medicine. We conducted a comprehensive search for articles published between 2019 and 2024, using the search string (Neural Architecture Search) OR (NAS) AND (medicine) OR (medical) OR (disease) OR (cardiovascular system) OR (MRI). We identified relevant studies published by Elsevier, IEEE, MDPI (IJERPH, Mathematics, Sensors), Nature, and SpringerLink, specifically focusing on experimental NAS applications in medical contexts. Data from 62 articles were collected, revealing a predominant use of NAS for image data classification, particularly in neurological research. Moreover, NAS demonstrated superior model performance compared with conventional deep learning methods. It is anticipated that future developments in NAS models for medical applications will lead to greater ease of use and enhanced efficacy as well as reduced computational resource consumption, thereby helping to mitigate health care workforce shortages and improve diagnostic accuracy. In addition to its application in diagnosis, NAS holds promise in everyday health monitoring, which could potentially enable the early detection of diseases, empowering people to receive the care that need and live healthier lives.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this article we examined research on using neural architecture search (NAS) in medical applications, prompted by the current shortage of health care professionals relative to patient volumes. We explored the current state of NAS development in various medical fields, evaluated its performance, and examined potential future directions of NAS in medicine. We conducted a comprehensive search for articles published between 2019 and 2024, using the search string (Neural Architecture Search) OR (NAS) AND (medicine) OR (medical) OR (disease) OR (cardiovascular system) OR (MRI). We identified relevant studies published by Elsevier, IEEE, MDPI (IJERPH, Mathematics, Sensors), Nature, and SpringerLink, specifically focusing on experimental NAS applications in medical contexts. Data from 62 articles were collected, revealing a predominant use of NAS for image data classification, particularly in neurological research. Moreover, NAS demonstrated superior model performance compared with conventional deep learning methods. It is anticipated that future developments in NAS models for medical applications will lead to greater ease of use and enhanced efficacy as well as reduced computational resource consumption, thereby helping to mitigate health care workforce shortages and improve diagnostic accuracy. In addition to its application in diagnosis, NAS holds promise in everyday health monitoring, which could potentially enable the early detection of diseases, empowering people to receive the care that need and live healthier lives.
18.
Sheng, Yi; Yang, Junhuan; Li, Jinyang; Alaina, James; Xu, Xiaowei; Shi, Yiyu; Hu, Jingtong; Jiang, Weiwen; Yang, Lei
Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset Proceedings Article
In: Linguraru, Marius George; Dou, Qi; Feragen, Aasa; Giannarou, Stamatia; Glocker, Ben; Lekadir, Karim; Schnabel, Julia A. (Ed.): Medical Image Computing and Computer Assisted Intervention - MICCAI 2024 - 27th International Conference, Marrakesh, Morocco, October 6-10, 2024, Proceedings, Part X, pp. 153–163, Springer, 2024.
@inproceedings{DBLP:conf/miccai/ShengYLAXSHJY24,
title = {Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset},
author = {Yi Sheng and Junhuan Yang and Jinyang Li and James Alaina and Xiaowei Xu and Yiyu Shi and Jingtong Hu and Weiwen Jiang and Lei Yang},
editor = {Marius George Linguraru and Qi Dou and Aasa Feragen and Stamatia Giannarou and Ben Glocker and Karim Lekadir and Julia A. Schnabel},
url = {https://doi.org/10.1007/978-3-031-72117-5_15},
doi = {10.1007/978-3-031-72117-5_15},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Medical Image Computing and Computer Assisted Intervention - MICCAI
2024 - 27th International Conference, Marrakesh, Morocco, October
6-10, 2024, Proceedings, Part X},
volume = {15010},
pages = {153–163},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
19.
Wei, Jiahong; Xue, Bing; Zhang, Mengjie
EZUAS: Evolutionary Zero-shot U-shape Architecture Search for Medical Image Segmentation Proceedings Article
In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 422–430, Association for Computing Machinery, Melbourne, VIC, Australia, 2024, ISBN: 9798400704949.
@inproceedings{10.1145/3638529.3654041,
title = {EZUAS: Evolutionary Zero-shot U-shape Architecture Search for Medical Image Segmentation},
author = {Jiahong Wei and Bing Xue and Mengjie Zhang},
url = {https://doi.org/10.1145/3638529.3654041},
doi = {10.1145/3638529.3654041},
isbn = {9798400704949},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
pages = {422–430},
publisher = {Association for Computing Machinery},
address = {Melbourne, VIC, Australia},
series = {GECCO '24},
abstract = {Recently, deep learning-based methods have become the mainstream for medical image segmentation. Since manually designing deep neural networks (DNNs) is laborious and time-consuming, neural architecture search (NAS) becomes a popular stream for automatically designing DNNs for medical image segmentation. However, existing NAS work for medical image segmentation is still computationally expensive. Given the limited computation power, it is not always applicable to search for a well-performing model from an enlarged search space. In this paper, we propose EZUAS, a novel method of evolutionary zero-shot NAS for medical image segmentation, to address these issues. First, a new search space is designed for the automated design of DNNs. A genetic algorithm (GA) with an aligned crossover operation is then leveraged to search the network architectures under the model complexity constraints to get performant and lightweight models. In addition, a variable-length integer encoding scheme is devised to encode the candidate U-shaped DNNs with different stages. We conduct experiments on two commonly used medical image segmentation datasets to verify the effectiveness of the proposed EZUAS. Compared with the state-of-the-art methods, the proposed method can find a model much faster (about 0.04 GPU day) and achieve the best performance with lower computational complexity.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Recently, deep learning-based methods have become the mainstream for medical image segmentation. Since manually designing deep neural networks (DNNs) is laborious and time-consuming, neural architecture search (NAS) becomes a popular stream for automatically designing DNNs for medical image segmentation. However, existing NAS work for medical image segmentation is still computationally expensive. Given the limited computation power, it is not always applicable to search for a well-performing model from an enlarged search space. In this paper, we propose EZUAS, a novel method of evolutionary zero-shot NAS for medical image segmentation, to address these issues. First, a new search space is designed for the automated design of DNNs. A genetic algorithm (GA) with an aligned crossover operation is then leveraged to search the network architectures under the model complexity constraints to get performant and lightweight models. In addition, a variable-length integer encoding scheme is devised to encode the candidate U-shaped DNNs with different stages. We conduct experiments on two commonly used medical image segmentation datasets to verify the effectiveness of the proposed EZUAS. Compared with the state-of-the-art methods, the proposed method can find a model much faster (about 0.04 GPU day) and achieve the best performance with lower computational complexity.
20.
Ali, Muhammad Junaid; Moalic, Laurent; Essaid, Mokhtar; Idoumghar, Lhassane
Evolutionary Neural Architecture Search for 2D and 3D Medical Image Classification Proceedings Article
In: Franco, Leonardo; Mulatier, Clélia; Paszynski, Maciej; Krzhizhanovskaya, Valeria V.; Dongarra, Jack J.; Sloot, Peter M. A. (Ed.): Computational Science – ICCS 2024, pp. 131–146, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-63751-3.
@inproceedings{10.1007/978-3-031-63751-3_9,
title = {Evolutionary Neural Architecture Search for 2D and 3D Medical Image Classification},
author = {Muhammad Junaid Ali and Laurent Moalic and Mokhtar Essaid and Lhassane Idoumghar},
editor = {Leonardo Franco and Clélia Mulatier and Maciej Paszynski and Valeria V. Krzhizhanovskaya and Jack J. Dongarra and Peter M. A. Sloot},
url = {https://link.springer.com/chapter/10.1007/978-3-031-63751-3_9},
isbn = {978-3-031-63751-3},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Computational Science – ICCS 2024},
pages = {131–146},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Designing deep learning architectures is a challenging and time-consuming task. To address this problem, Neural Architecture Search (NAS) which automatically searches for a network topology is used. While existing NAS methods mainly focus on image classification tasks, particularly 2D medical images, this study presents an evolutionary NAS approach for 2D and 3D Medical image classification. We defined two different search spaces for 2D and 3D datasets and performed a comparative study of different meta-heuristics used in different NAS studies. Moreover, zero-cost proxies have been used to evaluate the performance of deep neural networks, which helps reduce the searching cost of the overall approach. Furthermore, recognizing the importance of Data Augmentation (DA) in model generalization, we propose a genetic algorithm based automatic DA strategy to find the optimal DA policy. Experiments on MedMNIST benchmark and BreakHIS dataset demonstrate the effectiveness of our approach, showcasing competitive results compared to existing AutoML approaches. The source code of our proposed approach is available at https://github.com/Junaid199f/evo_nas_med_2d_3d.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Designing deep learning architectures is a challenging and time-consuming task. To address this problem, Neural Architecture Search (NAS) which automatically searches for a network topology is used. While existing NAS methods mainly focus on image classification tasks, particularly 2D medical images, this study presents an evolutionary NAS approach for 2D and 3D Medical image classification. We defined two different search spaces for 2D and 3D datasets and performed a comparative study of different meta-heuristics used in different NAS studies. Moreover, zero-cost proxies have been used to evaluate the performance of deep neural networks, which helps reduce the searching cost of the overall approach. Furthermore, recognizing the importance of Data Augmentation (DA) in model generalization, we propose a genetic algorithm based automatic DA strategy to find the optimal DA policy. Experiments on MedMNIST benchmark and BreakHIS dataset demonstrate the effectiveness of our approach, showcasing competitive results compared to existing AutoML approaches. The source code of our proposed approach is available at https://github.com/Junaid199f/evo_nas_med_2d_3d.
2025
137.
Yu, Jiandong; Li, Tongtong; Shi, Xuerong; Zhao, Ziyang; Chen, Miao; Zhang, Yu; Wang, Junyu; Yao, Zhijun; Fang, Lei; Hu, Bin
ETMO-NAS: An efficient two-step multimodal one-shot NAS for lung nodules classification Journal Article
In: Biomedical Signal Processing and Control, vol. 104, pp. 107479, 2025, ISSN: 1746-8094.
@article{YU2025107479,
title = {ETMO-NAS: An efficient two-step multimodal one-shot NAS for lung nodules classification},
author = {Jiandong Yu and Tongtong Li and Xuerong Shi and Ziyang Zhao and Miao Chen and Yu Zhang and Junyu Wang and Zhijun Yao and Lei Fang and Bin Hu},
url = {https://www.sciencedirect.com/science/article/pii/S1746809424015374},
doi = {https://doi.org/10.1016/j.bspc.2024.107479},
issn = {1746-8094},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Biomedical Signal Processing and Control},
volume = {104},
pages = {107479},
abstract = {Malignant lung nodules are the initial diagnostic manifestation of lung cancer. Accurate predictive classification of malignant from benign lung nodules can improve treatment efficacy and survival rate of lung cancer patients. Since current deep learning-based PET/CT pulmonary nodule-assisted diagnosis models typically rely on network architectures carefully designed by researchers, which require professional expertise and extensive prior knowledge. To combat these challenges, in this paper, we propose an efficient two-step multimodal one-shot NAS (ETMO-NAS) for searching high-performance network architectures for reliable and accurate classification of lung nodules for multimodal PET/CT data. Specifically, the step I focuses on fully training the performance of all candidate architectures in the search space using the sandwich rule and in-place distillation strategy. The step II aims to split the search space into multiple non-overlapping subsupernets by parallel operation edge decomposition strategy and then fine-tune the subsupernets further improve performance. Finally, the performance of ETMO-NAS was validated on a set of real clinical data. The experimental results show that the classification architecture searched by ETMO-NAS achieves the best performance with accuracy, precision, sensitivity, specificity, and F-1 score of 94.23%, 92.10%, 95.83%, 92.86% and 0.9388, respectively. In addition, compared with the classical CNN model and NAS model, ETMO-NAS performs better with the same inputs, but with only 1/33–1/5 of the parameters. This provides substantial evidence for the competitiveness of the model in classification tasks and presents a new approach for automated diagnosis of PET/CT pulmonary nodules. Code and models will be available at: https://github.com/yujiandong0002/ETMO-NAS.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Malignant lung nodules are the initial diagnostic manifestation of lung cancer. Accurate predictive classification of malignant from benign lung nodules can improve treatment efficacy and survival rate of lung cancer patients. Since current deep learning-based PET/CT pulmonary nodule-assisted diagnosis models typically rely on network architectures carefully designed by researchers, which require professional expertise and extensive prior knowledge. To combat these challenges, in this paper, we propose an efficient two-step multimodal one-shot NAS (ETMO-NAS) for searching high-performance network architectures for reliable and accurate classification of lung nodules for multimodal PET/CT data. Specifically, the step I focuses on fully training the performance of all candidate architectures in the search space using the sandwich rule and in-place distillation strategy. The step II aims to split the search space into multiple non-overlapping subsupernets by parallel operation edge decomposition strategy and then fine-tune the subsupernets further improve performance. Finally, the performance of ETMO-NAS was validated on a set of real clinical data. The experimental results show that the classification architecture searched by ETMO-NAS achieves the best performance with accuracy, precision, sensitivity, specificity, and F-1 score of 94.23%, 92.10%, 95.83%, 92.86% and 0.9388, respectively. In addition, compared with the classical CNN model and NAS model, ETMO-NAS performs better with the same inputs, but with only 1/33–1/5 of the parameters. This provides substantial evidence for the competitiveness of the model in classification tasks and presents a new approach for automated diagnosis of PET/CT pulmonary nodules. Code and models will be available at: https://github.com/yujiandong0002/ETMO-NAS.
136.
Wang, Weibo; Li, Hua
NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search Journal Article
In: Biomedical Signal Processing and Control, vol. 100, pp. 107022, 2025, ISSN: 1746-8094.
@article{WANG2025107022,
title = {NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search},
author = {Weibo Wang and Hua Li},
url = {https://www.sciencedirect.com/science/article/pii/S1746809424010802},
doi = {https://doi.org/10.1016/j.bspc.2024.107022},
issn = {1746-8094},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Biomedical Signal Processing and Control},
volume = {100},
pages = {107022},
abstract = {In the detection and recognition of lung nodules, pulmonary nodules vary in size and shape and contain many similar tissues and organs around them, leading to the problems of both missed detection and false detection in existing detection algorithms. Designing proprietary detection and recognition networks manually requires substantial professional expertise. This process is time-consuming and labour-intensive and leads to issues like parameter redundancy and improper feature selection. Therefore, this paper proposes a new pulmonary CAD (computer-aided diagnosis) system for pulmonary nodules, NAS FD Lung (Using the NAS approach to search deep FPN and DPN networks), that can automatically learn and generate a deep learning network tailored to pulmonary nodule detection and recognition task requirements. NAS FD Lung aims to use automatic search to generate deep learning networks in the auxiliary diagnosis of pulmonary nodules to replace the manual design of deep learning networks. NAS FD Lung comprises two automatic search networks: BM NAS-FPN (Using NAS methods to search for deep FPN structures with Binary operation and Matrix multiplication fusion methods) network for nodule detection and NAS-A-DPN (Using the NAS approach to search deep DPN networks with attention mechanism) for nodule identification. The proposed technique is tested on the LUNA16 dataset, and the experimental results show that the model is superior to many existing state-of-the-art approaches. The detection accuracy of lung nodules is 98.23%. Regarding the lung nodules classification, the accuracy, specificity, sensitivity, and AUC values achieved 96.32%,97.14%,95.82%, and 98.33%, respectively.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In the detection and recognition of lung nodules, pulmonary nodules vary in size and shape and contain many similar tissues and organs around them, leading to the problems of both missed detection and false detection in existing detection algorithms. Designing proprietary detection and recognition networks manually requires substantial professional expertise. This process is time-consuming and labour-intensive and leads to issues like parameter redundancy and improper feature selection. Therefore, this paper proposes a new pulmonary CAD (computer-aided diagnosis) system for pulmonary nodules, NAS FD Lung (Using the NAS approach to search deep FPN and DPN networks), that can automatically learn and generate a deep learning network tailored to pulmonary nodule detection and recognition task requirements. NAS FD Lung aims to use automatic search to generate deep learning networks in the auxiliary diagnosis of pulmonary nodules to replace the manual design of deep learning networks. NAS FD Lung comprises two automatic search networks: BM NAS-FPN (Using NAS methods to search for deep FPN structures with Binary operation and Matrix multiplication fusion methods) network for nodule detection and NAS-A-DPN (Using the NAS approach to search deep DPN networks with attention mechanism) for nodule identification. The proposed technique is tested on the LUNA16 dataset, and the experimental results show that the model is superior to many existing state-of-the-art approaches. The detection accuracy of lung nodules is 98.23%. Regarding the lung nodules classification, the accuracy, specificity, sensitivity, and AUC values achieved 96.32%,97.14%,95.82%, and 98.33%, respectively.
2024
135.
AL-Sabri, Raeed; Gao, Jianliang; Chen, Jiamin; Oloulade, Babatounde Moctard; Wu, Zhenpeng; Abdullah, Monir; Hu, Xiaohua
M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions Proceedings Article
In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1783-1788, IEEE Computer Society, Los Alamitos, CA, USA, 2024.
@inproceedings{10821927,
title = { M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions },
author = {Raeed AL-Sabri and Jianliang Gao and Jiamin Chen and Babatounde Moctard Oloulade and Zhenpeng Wu and Monir Abdullah and Xiaohua Hu},
url = {https://doi.ieeecomputersociety.org/10.1109/BIBM62325.2024.10821927},
doi = {10.1109/BIBM62325.2024.10821927},
year = {2024},
date = {2024-12-01},
urldate = {2024-12-01},
booktitle = {2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
pages = {1783-1788},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
abstract = {Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework’s superior performance compared to state-of-the-art models.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework’s superior performance compared to state-of-the-art models.
134.
(Ed.)
Medical Neural Architecture Search: Survey and Taxonomy Collection
2024.
@collection{Benmeziane-ijcai24a,
title = {Medical Neural Architecture Search: Survey and Taxonomy},
author = {Hadjer Benmeziane and Imane Hamzaoui and Kaoutar El Maghraoui and Kaoutar El Maghraoui},
url = {https://www.ijcai.org/proceedings/2024/0878.pdf},
year = {2024},
date = {2024-08-03},
urldate = {2024-08-03},
booktitle = {IJCAI 2024},
keywords = {},
pubstate = {published},
tppubtype = {collection}
}
133.
Zhang, Jinnian; Chen, Weijie; Joshi, Tanmayee; Uyanik, Meltem; Zhang, Xiaomin; Loh, Po-Ling; Jog, Varun; Bruce, Richard; Garrett, John; McMillan, Alan
RobMedNAS: searching robust neural network architectures for medical image synthesis Journal Article
In: Biomedical Physics & Engineering Express, vol. 10, no. 5, pp. 055029, 2024.
@article{Zhang_2024,
title = {RobMedNAS: searching robust neural network architectures for medical image synthesis},
author = {Jinnian Zhang and Weijie Chen and Tanmayee Joshi and Meltem Uyanik and Xiaomin Zhang and Po-Ling Loh and Varun Jog and Richard Bruce and John Garrett and Alan McMillan},
url = {https://dx.doi.org/10.1088/2057-1976/ad6e87},
doi = {10.1088/2057-1976/ad6e87},
year = {2024},
date = {2024-08-01},
urldate = {2024-08-01},
journal = {Biomedical Physics & Engineering Express},
volume = {10},
number = {5},
pages = {055029},
publisher = {IOP Publishing},
abstract = {Investigating U-Net model robustness in medical image synthesis against adversarial perturbations, this study introduces RobMedNAS, a neural architecture search strategy for identifying resilient U-Net configurations. Through retrospective analysis of synthesized CT from MRI data, employing Dice coefficient and mean absolute error metrics across critical anatomical areas, the study evaluates traditional U-Net models and RobMedNAS-optimized models under adversarial attacks. Findings demonstrate RobMedNAS’s efficacy in enhancing U-Net resilience without compromising on accuracy, proposing a novel pathway for robust medical image processing.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Investigating U-Net model robustness in medical image synthesis against adversarial perturbations, this study introduces RobMedNAS, a neural architecture search strategy for identifying resilient U-Net configurations. Through retrospective analysis of synthesized CT from MRI data, employing Dice coefficient and mean absolute error metrics across critical anatomical areas, the study evaluates traditional U-Net models and RobMedNAS-optimized models under adversarial attacks. Findings demonstrate RobMedNAS’s efficacy in enhancing U-Net resilience without compromising on accuracy, proposing a novel pathway for robust medical image processing.
132.
Asiimwe, Arnold; Das, William; Benmeziane, Hadjer; Maghraoui, Kaoutar El
EDGE2024, 2024.
@conference{Asiimwe-edge24a,
title = {EfficientMedSAM: Accelerating Medical Image Segmentation via Neural Architecture Search and Knowledge Distillation},
author = {Arnold Asiimwe and William Das and Hadjer Benmeziane and Kaoutar El Maghraoui},
url = {https://research.ibm.com/publications/efficientmedsam-accelerating-medical-image-segmentation-via-neural-architecture-search-and-knowledge-distillation},
year = {2024},
date = {2024-07-07},
urldate = {2024-07-07},
booktitle = {EDGE2024},
journal = {EDGE 2024},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
131.
Berezsky, O. M.; Liashchynskyi, P. B.
METHOD OF GENERATIVE-ADVERSARIAL NETWORKS SEARCHING ARCHITECTURES FOR BIOMEDICAL IMAGES SYNTHESIS Journal Article
In: Radio Electronics, Computer Science, Control, no. 1, pp. 104, 2024.
@article{Berezsky_Liashchynskyi_2024,
title = {METHOD OF GENERATIVE-ADVERSARIAL NETWORKS SEARCHING ARCHITECTURES FOR BIOMEDICAL IMAGES SYNTHESIS},
author = {O. M. Berezsky and P. B. Liashchynskyi},
url = {http://ric.zntu.edu.ua/article/view/300976},
doi = {10.15588/1607-3274-2024-1-10},
year = {2024},
date = {2024-04-01},
urldate = {2024-04-01},
journal = {Radio Electronics, Computer Science, Control},
number = {1},
pages = {104},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
130.
Rajesh, Chilukamari; Sadam, Ravichandra; Kumar, Sushil
Automated Deep Learning Models for Medical Image Segmentation and Denoising Proceedings Article
In: 2024 17th International Conference on Signal Processing and Communication System (ICSPCS), pp. 1-7, 2024.
@inproceedings{10815837,
title = {Automated Deep Learning Models for Medical Image Segmentation and Denoising},
author = {Chilukamari Rajesh and Ravichandra Sadam and Sushil Kumar},
url = {https://ieeexplore.ieee.org/abstract/document/10815837},
doi = {10.1109/ICSPCS63175.2024.10815837},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 17th International Conference on Signal Processing and Communication System (ICSPCS)},
pages = {1-7},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
129.
Franco-Gaona, Erick; Avila-Garcia, Maria-Susana; Cruz-Aceves, Ivan; Orocio-Garcia, Hiram-Efrain; Escobedo-Gordillo, Andres; Brieva, Jorge
Neural Architecture Search Using Trajectory Metaheuristics to Classify Coronary Stenosis Proceedings Article
In: 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM), pp. 1-4, 2024.
@inproceedings{10783513,
title = {Neural Architecture Search Using Trajectory Metaheuristics to Classify Coronary Stenosis},
author = {Erick Franco-Gaona and Maria-Susana Avila-Garcia and Ivan Cruz-Aceves and Hiram-Efrain Orocio-Garcia and Andres Escobedo-Gordillo and Jorge Brieva},
url = {https://ieeexplore.ieee.org/abstract/document/10783513},
doi = {10.1109/SIPAIM62974.2024.10783513},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM)},
pages = {1-4},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
128.
Kuş, Zeki; Kiraz, Berna; Aydin, Musa; Kiraz, Alper
BioNAS: Neural Architecture Search for Multiple Data Modalities in Biomedical Image Classification Proceedings Article
In: Garcia, Fausto P.; Jamil, Akhtar; Hameed, Alaa Ali; Ortis, Alessandro; Ramirez, Isaac Segovia (Ed.): Recent Trends and Advances in Artificial Intelligence, pp. 539–550, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-70924-1.
@inproceedings{10.1007/978-3-031-70924-1_41,
title = {BioNAS: Neural Architecture Search for Multiple Data Modalities in Biomedical Image Classification},
author = {Zeki Kuş and Berna Kiraz and Musa Aydin and Alper Kiraz},
editor = {Fausto P. Garcia and Akhtar Jamil and Alaa Ali Hameed and Alessandro Ortis and Isaac Segovia Ramirez},
isbn = {978-3-031-70924-1},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Recent Trends and Advances in Artificial Intelligence},
pages = {539–550},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Neural Architecture Search (NAS) for biomedical image classification has the potential to design highly efficient and accurate networks automatically for tasks from different modalities. This paper presents BioNAS, a new NAS approach designed for multi-modal biomedical image classification. Unlike other methods, BioNAS dynamically adjusts the number of stacks, modules, and feature maps in the network to improve both performance and complexity. The proposed approach utilizes an opposition-based differential evolution optimization technique to identify the optimal network structure. We have compared our methods on two public multi-class classification datasets with different data modalities: DermaMNIST and OrganCMNIST. BioNAS outperforms hand-designed networks, automatic machine learning frameworks, and most NAS studies in terms of accuracy (ACC) and area under the curve (AUC) on the OrganCMNIST and DermaMNIST datasets. The proposed networks significantly outperform all other methods on the DermaMNIST dataset, achieving accuracy improvements of up to 4.4 points and AUC improvements of up to 2.6 points, and also surpass other studies by up to 5.4 points in accuracy and 0.6 points in AUC on OrganCMNIST. Moreover, the proposed networks have fewer parameters than hand-designed architectures like ResNet-18 and ResNet-50. The results indicate that BioNAS has the potential to be an effective alternative to hand-designed networks and automatic frameworks, offering a competitive solution in the classification of biomedical images.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Neural Architecture Search (NAS) for biomedical image classification has the potential to design highly efficient and accurate networks automatically for tasks from different modalities. This paper presents BioNAS, a new NAS approach designed for multi-modal biomedical image classification. Unlike other methods, BioNAS dynamically adjusts the number of stacks, modules, and feature maps in the network to improve both performance and complexity. The proposed approach utilizes an opposition-based differential evolution optimization technique to identify the optimal network structure. We have compared our methods on two public multi-class classification datasets with different data modalities: DermaMNIST and OrganCMNIST. BioNAS outperforms hand-designed networks, automatic machine learning frameworks, and most NAS studies in terms of accuracy (ACC) and area under the curve (AUC) on the OrganCMNIST and DermaMNIST datasets. The proposed networks significantly outperform all other methods on the DermaMNIST dataset, achieving accuracy improvements of up to 4.4 points and AUC improvements of up to 2.6 points, and also surpass other studies by up to 5.4 points in accuracy and 0.6 points in AUC on OrganCMNIST. Moreover, the proposed networks have fewer parameters than hand-designed architectures like ResNet-18 and ResNet-50. The results indicate that BioNAS has the potential to be an effective alternative to hand-designed networks and automatic frameworks, offering a competitive solution in the classification of biomedical images.
127.
Ji, Yuanfeng; 纪源丰,
Towards efficient deep learning for medical image analysis Bachelor Thesis
2024.
@bachelorthesis{HKUHUB_10722_350322,
title = {Towards efficient deep learning for medical image analysis},
author = {Yuanfeng Ji and 纪源丰},
url = {http://hdl.handle.net/10722/350322},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {HKU Theses Online (HKUTO)},
abstract = {The advancement of deep learning in medical image analysis has revolutionized the field of medical diagnostics, improving both the accuracy and efficiency of computer- aided diagnostic systems. Despite substantial progress, significant challenges remain, mainly due to the diversity of medical tasks and the scarcity of high-quality annotated data. This thesis addresses these challenges by proposing efficient deep learning meth- ods that improve the development and evaluation of medical imaging models, ensuring their reliability and effectiveness in clinical settings. First, the thesis presents the Ab- dominal Multi-Organ Segmentation (AMOS) dataset, a robust collection of annotated medical images from different demographics and imaging modalities. AMOS utilizes a semi-automated annotation process powered by a pre-trained model, which not only accelerates annotation, but also improves the accuracy and consistency of the data. This approach helps curate a comprehensive benchmark that reflects the real-world complexity and variability of clinical environments, facilitating rigorous testing and evaluation of medical deep learning applications. Then, to address the diversity of imaging tasks, this thesis presents UXNet, a novel application of Neural Architecture Search (NAS) technology designed to adapt neural network architectures specifically for different medical image analysis tasks. UXNet dynamically adapts to the specifics of the input data and output tasks, optimizing model structures to achieve high levels of accuracy and efficiency in different settings. This reduces the reliance on manual tuning and expert knowledge, streamlining the development process of deep learn- ing solutions for medical imaging. Moreover, recognizing the increasing complexity of deep learning models, the thesis introduces AutoBench, an automated tool for the assessment and governance of these models. Leveraging large language models, Au- toBench automates the creation of evaluation standards and conducts comprehensive performance evaluations, facilitating continuous monitoring and adaptation of model performance in medical applications. Finally, I discuss some future work towards in developing efficient and effective deep learning medical applications.},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
The advancement of deep learning in medical image analysis has revolutionized the field of medical diagnostics, improving both the accuracy and efficiency of computer- aided diagnostic systems. Despite substantial progress, significant challenges remain, mainly due to the diversity of medical tasks and the scarcity of high-quality annotated data. This thesis addresses these challenges by proposing efficient deep learning meth- ods that improve the development and evaluation of medical imaging models, ensuring their reliability and effectiveness in clinical settings. First, the thesis presents the Ab- dominal Multi-Organ Segmentation (AMOS) dataset, a robust collection of annotated medical images from different demographics and imaging modalities. AMOS utilizes a semi-automated annotation process powered by a pre-trained model, which not only accelerates annotation, but also improves the accuracy and consistency of the data. This approach helps curate a comprehensive benchmark that reflects the real-world complexity and variability of clinical environments, facilitating rigorous testing and evaluation of medical deep learning applications. Then, to address the diversity of imaging tasks, this thesis presents UXNet, a novel application of Neural Architecture Search (NAS) technology designed to adapt neural network architectures specifically for different medical image analysis tasks. UXNet dynamically adapts to the specifics of the input data and output tasks, optimizing model structures to achieve high levels of accuracy and efficiency in different settings. This reduces the reliance on manual tuning and expert knowledge, streamlining the development process of deep learn- ing solutions for medical imaging. Moreover, recognizing the increasing complexity of deep learning models, the thesis introduces AutoBench, an automated tool for the assessment and governance of these models. Leveraging large language models, Au- toBench automates the creation of evaluation standards and conducts comprehensive performance evaluations, facilitating continuous monitoring and adaptation of model performance in medical applications. Finally, I discuss some future work towards in developing efficient and effective deep learning medical applications.
126.
Chen, Xi; Lv, Jiahuan; Wang, Zeyu; Qin, Genggeng; Zhou, Zhiguo
In: Computers in Biology and Medicine, vol. 183, pp. 109299, 2024, ISSN: 0010-4825.
@article{CHEN2024109299,
title = {Deep-AutoMO: Deep automated multiobjective neural network for trustworthy lesion malignancy diagnosis in the early stage via digital breast tomosynthesis},
author = {Xi Chen and Jiahuan Lv and Zeyu Wang and Genggeng Qin and Zhiguo Zhou},
url = {https://www.sciencedirect.com/science/article/pii/S0010482524013842},
doi = {https://doi.org/10.1016/j.compbiomed.2024.109299},
issn = {0010-4825},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Computers in Biology and Medicine},
volume = {183},
pages = {109299},
abstract = {Breast cancer is the most prevalent cancer in women, and early diagnosis of malignant lesions is crucial for developing treatment plans. Digital breast tomosynthesis (DBT) has emerged as a valuable tool for early breast cancer detection, as it can identify more lesions and improve the early detection rate. Deep learning has shown great potential in medical image-based cancer diagnosis, including DBT. However, deploying these models in clinical practice may be challenging due to concerns about reliability and robustness. In this study, we developed a novel deep automated multiobjective neural network (Deep-AutoMO) to build a trustworthy model and achieve balance, safety and robustness in a unified way. During the training stage, we introduced a multiobjective immune neural architecture search (MINAS) that simultaneously considers sensitivity and specificity as objective functions, aiming to strike a balance between the two. Each neural network in Deep-AutoMO comprises a combination of a ResNet block, a DenseNet block and a pooling layer. We employ Bayesian optimization to optimize the hyperparameters in the MINAS, enhancing the efficiency of the model training process. In the testing stage, evidential reasoning based on entropy (ERE) approach is proposed to build a safe and robust model. The experimental study on DBT images demonstrated that Deep-AutoMO achieves promising performance with a well-balanced trade-off between sensitivity and specificity, outperforming currently available methods. Moreover, the model's safety is ensured through uncertainty estimation, and its robustness is improved, making it a trustworthy tool for breast cancer diagnosis in clinical settings. We have shared the code on GitHub for other researchers to use. The code can be found at https://github.com/ChaoyangZhang-XJTU/Deep-AutoMO.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Breast cancer is the most prevalent cancer in women, and early diagnosis of malignant lesions is crucial for developing treatment plans. Digital breast tomosynthesis (DBT) has emerged as a valuable tool for early breast cancer detection, as it can identify more lesions and improve the early detection rate. Deep learning has shown great potential in medical image-based cancer diagnosis, including DBT. However, deploying these models in clinical practice may be challenging due to concerns about reliability and robustness. In this study, we developed a novel deep automated multiobjective neural network (Deep-AutoMO) to build a trustworthy model and achieve balance, safety and robustness in a unified way. During the training stage, we introduced a multiobjective immune neural architecture search (MINAS) that simultaneously considers sensitivity and specificity as objective functions, aiming to strike a balance between the two. Each neural network in Deep-AutoMO comprises a combination of a ResNet block, a DenseNet block and a pooling layer. We employ Bayesian optimization to optimize the hyperparameters in the MINAS, enhancing the efficiency of the model training process. In the testing stage, evidential reasoning based on entropy (ERE) approach is proposed to build a safe and robust model. The experimental study on DBT images demonstrated that Deep-AutoMO achieves promising performance with a well-balanced trade-off between sensitivity and specificity, outperforming currently available methods. Moreover, the model's safety is ensured through uncertainty estimation, and its robustness is improved, making it a trustworthy tool for breast cancer diagnosis in clinical settings. We have shared the code on GitHub for other researchers to use. The code can be found at https://github.com/ChaoyangZhang-XJTU/Deep-AutoMO.
125.
Park, Eunbin; Lee, Youngjoo
mDARTS: Searching ML-Based ECG Classifiers against Membership Inference Attacks Journal Article
In: IEEE Journal of Biomedical and Health Informatics, pp. 1-11, 2024.
@article{10720208,
title = {mDARTS: Searching ML-Based ECG Classifiers against Membership Inference Attacks},
author = {Eunbin Park and Youngjoo Lee},
url = {https://ieeexplore.ieee.org/abstract/document/10720208},
doi = {10.1109/JBHI.2024.3481505},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {1-11},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
124.
Zhang, Qing; Shao, Dan; Lin, Lin; Gong, Guoliang; Xu, Rui; Kido, Shoji; Cui, HongWei
Feature Separation in Diffuse Lung Disease Image Classification by Using Evolutionary Algorithm-Based NAS Journal Article
In: IEEE Journal of Biomedical and Health Informatics, pp. 1-12, 2024.
@article{10716760,
title = {Feature Separation in Diffuse Lung Disease Image Classification by Using Evolutionary Algorithm-Based NAS},
author = {Qing Zhang and Dan Shao and Lin Lin and Guoliang Gong and Rui Xu and Shoji Kido and HongWei Cui},
url = {https://ieeexplore.ieee.org/document/10716760},
doi = {10.1109/JBHI.2024.3481012},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {1-12},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
123.
Wu, Yu; Fan, Hailong; Ying, Weiqin; Zhou, Zekun; Zheng, Qiaoqiao; Zhang, Jiajian
Federated Neural Architecture Search with Hierarchical Progressive Acceleration for Medical Image Segmentation Proceedings Article
In: Tan, Ying; Shi, Yuhui (Ed.): Advances in Swarm Intelligence, pp. 112–123, Springer Nature Singapore, Singapore, 2024, ISBN: 978-981-97-7184-4.
@inproceedings{10.1007/978-981-97-7184-4_10,
title = {Federated Neural Architecture Search with Hierarchical Progressive Acceleration for Medical Image Segmentation},
author = {Yu Wu and Hailong Fan and Weiqin Ying and Zekun Zhou and Qiaoqiao Zheng and Jiajian Zhang},
editor = {Ying Tan and Yuhui Shi},
url = {https://link.springer.com/chapter/10.1007/978-981-97-7184-4_10},
isbn = {978-981-97-7184-4},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Advances in Swarm Intelligence},
pages = {112–123},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {Deep neural networks for medical image segmentation often require data from multiple medical institutions, but privacy concerns limit data sharing, making federated learning (FL) a viable alternative. However, predefined network architectures in FL are often suboptimal and need extensive manual tuning. Traditional neural architecture search (NAS) methods are unsuitable for FL due to high communication and evaluation costs. This paper presents an evolutionary NAS method (FS-ENAS) for federated medical image segmentation. FS-ENAS utilizes a U-Net++ based supernet with depthwise separable convolution and adaptable skip connections. It introduces a novel multi-stage, hierarchical progressive acceleration strategy tailored for federated neural architecture search to reduce communication and evaluation burdens. Experimental results on retinal blood vessel segmentation tasks show that FS-ENAS efficiently searches for suitable architectures with reduced communication and evaluation costs while protecting privacy.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Deep neural networks for medical image segmentation often require data from multiple medical institutions, but privacy concerns limit data sharing, making federated learning (FL) a viable alternative. However, predefined network architectures in FL are often suboptimal and need extensive manual tuning. Traditional neural architecture search (NAS) methods are unsuitable for FL due to high communication and evaluation costs. This paper presents an evolutionary NAS method (FS-ENAS) for federated medical image segmentation. FS-ENAS utilizes a U-Net++ based supernet with depthwise separable convolution and adaptable skip connections. It introduces a novel multi-stage, hierarchical progressive acceleration strategy tailored for federated neural architecture search to reduce communication and evaluation burdens. Experimental results on retinal blood vessel segmentation tasks show that FS-ENAS efficiently searches for suitable architectures with reduced communication and evaluation costs while protecting privacy.
122.
Domanski, Peter; Ray, Aritra; Lafata, Kyle; Firouzi, Farshad; Chakrabarty, Krishnendu; Pflüger, Dirk
Advancing blood glucose prediction with neural architecture search and deep reinforcement learning for type 1 diabetics Journal Article
In: Biocybernetics and Biomedical Engineering, vol. 44, no. 3, pp. 481-500, 2024, ISSN: 0208-5216.
@article{DOMANSKI2024481,
title = {Advancing blood glucose prediction with neural architecture search and deep reinforcement learning for type 1 diabetics},
author = {Peter Domanski and Aritra Ray and Kyle Lafata and Farshad Firouzi and Krishnendu Chakrabarty and Dirk Pflüger},
url = {https://www.sciencedirect.com/science/article/pii/S0208521624000536},
doi = {https://doi.org/10.1016/j.bbe.2024.07.006},
issn = {0208-5216},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Biocybernetics and Biomedical Engineering},
volume = {44},
number = {3},
pages = {481-500},
abstract = {For individuals with Type-1 diabetes mellitus, accurate prediction of future blood glucose values is crucial to aid its regulation with insulin administration, tailored to the individual’s specific needs. The authors propose a novel approach for the integration of a neural architecture search framework with deep reinforcement learning to autonomously generate and train architectures, optimized for each subject over model size and analytical prediction performance, for the blood glucose prediction task in individuals with Type-1 diabetes. The authors evaluate the proposed approach on the OhioT1DM dataset, which includes blood glucose monitoring records at 5-min intervals over 8 weeks for 12 patients with Type-1 diabetes mellitus. Prior work focused on predicting blood glucose levels in 30 and 45-min prediction horizons, equivalent to 6 and 9 data points, respectively. Compared to the previously achieved best error, the proposed method demonstrates improvements of 18.4 % and 22.5 % on average for mean absolute error in the 30-min and 45-min prediction horizons, respectively, through the proposed deep reinforcement learning framework. Using the deep reinforcement learning framework, the best-case and worst-case analytical performance measured over root mean square error and mean absolute error was obtained for subject ID 570 and subject ID 584, respectively. Models optimized for performance on the prediction task and model size were obtained after implementing neural architecture search in conjunction with deep reinforcement learning on these two extreme cases. The authors demonstrate improvements of 4.8 % using Long Short Term Memory-based architectures and 5.7 % with Gated Recurrent Units-based architectures for patient ID 570 on the analytical prediction performance by integrating neural architecture search with deep reinforcement learning framework. The patient with the lowest performance (ID 584) on the deep reinforcement learning method had an even greater performance boost, with improvements of 10.0 % and 12.6 % observed for the Long Short-Term Memory and Gated Recurrent Units, respectively. The subject-specific optimized models over performance and model size from the neural architecture search in conjunction with deep reinforcement learning had a reduction in model size which ranged from 20 to 150 times compared to the model obtained using only the deep reinforcement learning method. The smaller size, indicating a reduction in model complexity in terms of the number of trainable network parameters, was achieved without a loss in the prediction performance.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
For individuals with Type-1 diabetes mellitus, accurate prediction of future blood glucose values is crucial to aid its regulation with insulin administration, tailored to the individual’s specific needs. The authors propose a novel approach for the integration of a neural architecture search framework with deep reinforcement learning to autonomously generate and train architectures, optimized for each subject over model size and analytical prediction performance, for the blood glucose prediction task in individuals with Type-1 diabetes. The authors evaluate the proposed approach on the OhioT1DM dataset, which includes blood glucose monitoring records at 5-min intervals over 8 weeks for 12 patients with Type-1 diabetes mellitus. Prior work focused on predicting blood glucose levels in 30 and 45-min prediction horizons, equivalent to 6 and 9 data points, respectively. Compared to the previously achieved best error, the proposed method demonstrates improvements of 18.4 % and 22.5 % on average for mean absolute error in the 30-min and 45-min prediction horizons, respectively, through the proposed deep reinforcement learning framework. Using the deep reinforcement learning framework, the best-case and worst-case analytical performance measured over root mean square error and mean absolute error was obtained for subject ID 570 and subject ID 584, respectively. Models optimized for performance on the prediction task and model size were obtained after implementing neural architecture search in conjunction with deep reinforcement learning on these two extreme cases. The authors demonstrate improvements of 4.8 % using Long Short Term Memory-based architectures and 5.7 % with Gated Recurrent Units-based architectures for patient ID 570 on the analytical prediction performance by integrating neural architecture search with deep reinforcement learning framework. The patient with the lowest performance (ID 584) on the deep reinforcement learning method had an even greater performance boost, with improvements of 10.0 % and 12.6 % observed for the Long Short-Term Memory and Gated Recurrent Units, respectively. The subject-specific optimized models over performance and model size from the neural architecture search in conjunction with deep reinforcement learning had a reduction in model size which ranged from 20 to 150 times compared to the model obtained using only the deep reinforcement learning method. The smaller size, indicating a reduction in model complexity in terms of the number of trainable network parameters, was achieved without a loss in the prediction performance.
121.
Chaiyarin, Sinee; Rojbundit, Napassorn; Piyabenjarad, Panichanok; Limpitigranon, Pimpattra; Wisitthipakdeekul, Siraprapa; Nonthasaen, Pawaree; Achararit, Paniti
Neural architecture search for medicine: A survey Journal Article
In: Informatics in Medicine Unlocked, vol. 50, pp. 101565, 2024, ISSN: 2352-9148.
@article{CHAIYARIN2024101565,
title = {Neural architecture search for medicine: A survey},
author = {Sinee Chaiyarin and Napassorn Rojbundit and Panichanok Piyabenjarad and Pimpattra Limpitigranon and Siraprapa Wisitthipakdeekul and Pawaree Nonthasaen and Paniti Achararit},
url = {https://www.sciencedirect.com/science/article/pii/S2352914824001217},
doi = {https://doi.org/10.1016/j.imu.2024.101565},
issn = {2352-9148},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Informatics in Medicine Unlocked},
volume = {50},
pages = {101565},
abstract = {In this article we examined research on using neural architecture search (NAS) in medical applications, prompted by the current shortage of health care professionals relative to patient volumes. We explored the current state of NAS development in various medical fields, evaluated its performance, and examined potential future directions of NAS in medicine. We conducted a comprehensive search for articles published between 2019 and 2024, using the search string (Neural Architecture Search) OR (NAS) AND (medicine) OR (medical) OR (disease) OR (cardiovascular system) OR (MRI). We identified relevant studies published by Elsevier, IEEE, MDPI (IJERPH, Mathematics, Sensors), Nature, and SpringerLink, specifically focusing on experimental NAS applications in medical contexts. Data from 62 articles were collected, revealing a predominant use of NAS for image data classification, particularly in neurological research. Moreover, NAS demonstrated superior model performance compared with conventional deep learning methods. It is anticipated that future developments in NAS models for medical applications will lead to greater ease of use and enhanced efficacy as well as reduced computational resource consumption, thereby helping to mitigate health care workforce shortages and improve diagnostic accuracy. In addition to its application in diagnosis, NAS holds promise in everyday health monitoring, which could potentially enable the early detection of diseases, empowering people to receive the care that need and live healthier lives.},
keywords = {},
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}
In this article we examined research on using neural architecture search (NAS) in medical applications, prompted by the current shortage of health care professionals relative to patient volumes. We explored the current state of NAS development in various medical fields, evaluated its performance, and examined potential future directions of NAS in medicine. We conducted a comprehensive search for articles published between 2019 and 2024, using the search string (Neural Architecture Search) OR (NAS) AND (medicine) OR (medical) OR (disease) OR (cardiovascular system) OR (MRI). We identified relevant studies published by Elsevier, IEEE, MDPI (IJERPH, Mathematics, Sensors), Nature, and SpringerLink, specifically focusing on experimental NAS applications in medical contexts. Data from 62 articles were collected, revealing a predominant use of NAS for image data classification, particularly in neurological research. Moreover, NAS demonstrated superior model performance compared with conventional deep learning methods. It is anticipated that future developments in NAS models for medical applications will lead to greater ease of use and enhanced efficacy as well as reduced computational resource consumption, thereby helping to mitigate health care workforce shortages and improve diagnostic accuracy. In addition to its application in diagnosis, NAS holds promise in everyday health monitoring, which could potentially enable the early detection of diseases, empowering people to receive the care that need and live healthier lives.
120.
Sheng, Yi; Yang, Junhuan; Li, Jinyang; Alaina, James; Xu, Xiaowei; Shi, Yiyu; Hu, Jingtong; Jiang, Weiwen; Yang, Lei
Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset Proceedings Article
In: Linguraru, Marius George; Dou, Qi; Feragen, Aasa; Giannarou, Stamatia; Glocker, Ben; Lekadir, Karim; Schnabel, Julia A. (Ed.): Medical Image Computing and Computer Assisted Intervention - MICCAI 2024 - 27th International Conference, Marrakesh, Morocco, October 6-10, 2024, Proceedings, Part X, pp. 153–163, Springer, 2024.
@inproceedings{DBLP:conf/miccai/ShengYLAXSHJY24,
title = {Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset},
author = {Yi Sheng and Junhuan Yang and Jinyang Li and James Alaina and Xiaowei Xu and Yiyu Shi and Jingtong Hu and Weiwen Jiang and Lei Yang},
editor = {Marius George Linguraru and Qi Dou and Aasa Feragen and Stamatia Giannarou and Ben Glocker and Karim Lekadir and Julia A. Schnabel},
url = {https://doi.org/10.1007/978-3-031-72117-5_15},
doi = {10.1007/978-3-031-72117-5_15},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Medical Image Computing and Computer Assisted Intervention - MICCAI
2024 - 27th International Conference, Marrakesh, Morocco, October
6-10, 2024, Proceedings, Part X},
volume = {15010},
pages = {153–163},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
119.
Wei, Jiahong; Xue, Bing; Zhang, Mengjie
EZUAS: Evolutionary Zero-shot U-shape Architecture Search for Medical Image Segmentation Proceedings Article
In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 422–430, Association for Computing Machinery, Melbourne, VIC, Australia, 2024, ISBN: 9798400704949.
@inproceedings{10.1145/3638529.3654041,
title = {EZUAS: Evolutionary Zero-shot U-shape Architecture Search for Medical Image Segmentation},
author = {Jiahong Wei and Bing Xue and Mengjie Zhang},
url = {https://doi.org/10.1145/3638529.3654041},
doi = {10.1145/3638529.3654041},
isbn = {9798400704949},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
pages = {422–430},
publisher = {Association for Computing Machinery},
address = {Melbourne, VIC, Australia},
series = {GECCO '24},
abstract = {Recently, deep learning-based methods have become the mainstream for medical image segmentation. Since manually designing deep neural networks (DNNs) is laborious and time-consuming, neural architecture search (NAS) becomes a popular stream for automatically designing DNNs for medical image segmentation. However, existing NAS work for medical image segmentation is still computationally expensive. Given the limited computation power, it is not always applicable to search for a well-performing model from an enlarged search space. In this paper, we propose EZUAS, a novel method of evolutionary zero-shot NAS for medical image segmentation, to address these issues. First, a new search space is designed for the automated design of DNNs. A genetic algorithm (GA) with an aligned crossover operation is then leveraged to search the network architectures under the model complexity constraints to get performant and lightweight models. In addition, a variable-length integer encoding scheme is devised to encode the candidate U-shaped DNNs with different stages. We conduct experiments on two commonly used medical image segmentation datasets to verify the effectiveness of the proposed EZUAS. Compared with the state-of-the-art methods, the proposed method can find a model much faster (about 0.04 GPU day) and achieve the best performance with lower computational complexity.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Recently, deep learning-based methods have become the mainstream for medical image segmentation. Since manually designing deep neural networks (DNNs) is laborious and time-consuming, neural architecture search (NAS) becomes a popular stream for automatically designing DNNs for medical image segmentation. However, existing NAS work for medical image segmentation is still computationally expensive. Given the limited computation power, it is not always applicable to search for a well-performing model from an enlarged search space. In this paper, we propose EZUAS, a novel method of evolutionary zero-shot NAS for medical image segmentation, to address these issues. First, a new search space is designed for the automated design of DNNs. A genetic algorithm (GA) with an aligned crossover operation is then leveraged to search the network architectures under the model complexity constraints to get performant and lightweight models. In addition, a variable-length integer encoding scheme is devised to encode the candidate U-shaped DNNs with different stages. We conduct experiments on two commonly used medical image segmentation datasets to verify the effectiveness of the proposed EZUAS. Compared with the state-of-the-art methods, the proposed method can find a model much faster (about 0.04 GPU day) and achieve the best performance with lower computational complexity.
118.
Ali, Muhammad Junaid; Moalic, Laurent; Essaid, Mokhtar; Idoumghar, Lhassane
Evolutionary Neural Architecture Search for 2D and 3D Medical Image Classification Proceedings Article
In: Franco, Leonardo; Mulatier, Clélia; Paszynski, Maciej; Krzhizhanovskaya, Valeria V.; Dongarra, Jack J.; Sloot, Peter M. A. (Ed.): Computational Science – ICCS 2024, pp. 131–146, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-63751-3.
@inproceedings{10.1007/978-3-031-63751-3_9,
title = {Evolutionary Neural Architecture Search for 2D and 3D Medical Image Classification},
author = {Muhammad Junaid Ali and Laurent Moalic and Mokhtar Essaid and Lhassane Idoumghar},
editor = {Leonardo Franco and Clélia Mulatier and Maciej Paszynski and Valeria V. Krzhizhanovskaya and Jack J. Dongarra and Peter M. A. Sloot},
url = {https://link.springer.com/chapter/10.1007/978-3-031-63751-3_9},
isbn = {978-3-031-63751-3},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Computational Science – ICCS 2024},
pages = {131–146},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Designing deep learning architectures is a challenging and time-consuming task. To address this problem, Neural Architecture Search (NAS) which automatically searches for a network topology is used. While existing NAS methods mainly focus on image classification tasks, particularly 2D medical images, this study presents an evolutionary NAS approach for 2D and 3D Medical image classification. We defined two different search spaces for 2D and 3D datasets and performed a comparative study of different meta-heuristics used in different NAS studies. Moreover, zero-cost proxies have been used to evaluate the performance of deep neural networks, which helps reduce the searching cost of the overall approach. Furthermore, recognizing the importance of Data Augmentation (DA) in model generalization, we propose a genetic algorithm based automatic DA strategy to find the optimal DA policy. Experiments on MedMNIST benchmark and BreakHIS dataset demonstrate the effectiveness of our approach, showcasing competitive results compared to existing AutoML approaches. The source code of our proposed approach is available at https://github.com/Junaid199f/evo_nas_med_2d_3d.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Designing deep learning architectures is a challenging and time-consuming task. To address this problem, Neural Architecture Search (NAS) which automatically searches for a network topology is used. While existing NAS methods mainly focus on image classification tasks, particularly 2D medical images, this study presents an evolutionary NAS approach for 2D and 3D Medical image classification. We defined two different search spaces for 2D and 3D datasets and performed a comparative study of different meta-heuristics used in different NAS studies. Moreover, zero-cost proxies have been used to evaluate the performance of deep neural networks, which helps reduce the searching cost of the overall approach. Furthermore, recognizing the importance of Data Augmentation (DA) in model generalization, we propose a genetic algorithm based automatic DA strategy to find the optimal DA policy. Experiments on MedMNIST benchmark and BreakHIS dataset demonstrate the effectiveness of our approach, showcasing competitive results compared to existing AutoML approaches. The source code of our proposed approach is available at https://github.com/Junaid199f/evo_nas_med_2d_3d.
117.
Cardoso, Fabio; Vellasco, Marley; Figueiredo, Karla
Comparative Study Between Q-NAS and Traditional CNNs for Brain Tumor Classification Proceedings Article
In: Iliadis, Lazaros; Maglogiannis, Ilias; Papaleonidas, Antonios; Pimenidis, Elias; Jayne, Chrisina (Ed.): Engineering Applications of Neural Networks, pp. 93–105, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-62495-7.
@inproceedings{10.1007/978-3-031-62495-7_8,
title = {Comparative Study Between Q-NAS and Traditional CNNs for Brain Tumor Classification},
author = {Fabio Cardoso and Marley Vellasco and Karla Figueiredo},
editor = {Lazaros Iliadis and Ilias Maglogiannis and Antonios Papaleonidas and Elias Pimenidis and Chrisina Jayne},
url = {https://link.springer.com/chapter/10.1007/978-3-031-62495-7_8},
isbn = {978-3-031-62495-7},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Engineering Applications of Neural Networks},
pages = {93–105},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Brain tumours caused approximately 251,329 deaths worldwide in 2020, with the primary diagnostic method for these tumours involving medical imaging. In recent years, many works and applications have observed the use of Artificial Intelligence-based models using Convolution Neural Networks (CNNs) to identify health problems using images. In our study, we searched for new architectures based on CNN using the Q-NAS algorithm. We compared its performance and number of parameters with traditional architectures such as VGG, ResNet, and MobileNet to classify types of brain tumors in MRI images. The best architecture found by Q-NAS achieved an accuracy of 92% on the test data set, with a model with less than one million parameters, which is much smaller than that found in the selected traditional architectures for this study. It shows the potential of the Q-NAS algorithm and highlights the importance of efficient model design in the context of accurate and feature-aware medical image analysis.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Brain tumours caused approximately 251,329 deaths worldwide in 2020, with the primary diagnostic method for these tumours involving medical imaging. In recent years, many works and applications have observed the use of Artificial Intelligence-based models using Convolution Neural Networks (CNNs) to identify health problems using images. In our study, we searched for new architectures based on CNN using the Q-NAS algorithm. We compared its performance and number of parameters with traditional architectures such as VGG, ResNet, and MobileNet to classify types of brain tumors in MRI images. The best architecture found by Q-NAS achieved an accuracy of 92% on the test data set, with a model with less than one million parameters, which is much smaller than that found in the selected traditional architectures for this study. It shows the potential of the Q-NAS algorithm and highlights the importance of efficient model design in the context of accurate and feature-aware medical image analysis.
116.
Kaur, Tanvir; Kamboj, Shivani; Singh, Lovedeep; Tamanna,
Advanced YOLO-NAS-Based Detection and Screening of Brain Tumors Using Medical Diagnostic Proceedings Article
In: 2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), pp. 1-6, 2024.
@inproceedings{10531625,
title = {Advanced YOLO-NAS-Based Detection and Screening of Brain Tumors Using Medical Diagnostic},
author = {Tanvir Kaur and Shivani Kamboj and Lovedeep Singh and Tamanna},
url = {https://ieeexplore.ieee.org/abstract/document/10531625},
doi = {10.1109/AIMLA59606.2024.10531625},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA)},
pages = {1-6},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
115.
Saeedizadeh, Narges; Jalali, Seyed Mohammad Jafar; Khan, Burhan; Kebria, Parham Mohsenzadeh; Mohamed, Shady
A new optimization approach based on neural architecture search to enhance deep U-Net for efficient road segmentation Journal Article
In: Knowledge-Based Systems, vol. 296, pp. 111966, 2024, ISSN: 0950-7051.
@article{SAEEDIZADEH2024111966,
title = {A new optimization approach based on neural architecture search to enhance deep U-Net for efficient road segmentation},
author = {Narges Saeedizadeh and Seyed Mohammad Jafar Jalali and Burhan Khan and Parham Mohsenzadeh Kebria and Shady Mohamed},
url = {https://www.sciencedirect.com/science/article/pii/S0950705124006002},
doi = {https://doi.org/10.1016/j.knosys.2024.111966},
issn = {0950-7051},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Knowledge-Based Systems},
volume = {296},
pages = {111966},
abstract = {Neural Architecture Search (NAS) has significantly improved the accuracy of image classification and segmentation. However, these methods concentrate on finding segmentation structures for natural or medical applications. In this study, we introduce a NAS approach based on gradient optimization to identify ideal cell designs for road segmentation. To the best of our knowledge, this work represents the first application of gradient-based NAS to road extraction. Taking insight from the U-Net model and its successful variations in different image segmentation tasks, we propose NAS-enhanced U-Net, illustrated by an equal number of cells in both encoder and decoder levels. While cross-entropy combined with dice loss is commonly used in many segmentation tasks, road extraction brings up a unique challenge due to class imbalance. To address this, we introduce a combination of loss function. This function merges cross-entropy with weighted Dice loss, focusing on elevating the importance of the road class by assigning it a weight (⍵), while background Dice values are disregarded. The results indicate that the optimal weight for the proposed model equals 2. Additionally, our work challenges the assumption that increased model parameters or depth inherently leads to improved performance. Therefore, we establish search spaces 2,3,4,5,6,7 and 8 to automatically choose the optimal depth for model. We present promising segmentation results for our proposed method, achieved without any pretraining on the Massachusetts road dataset. Furthermore, these results are compared with those of 14 models categorized into four groups: U-Net, Segnet, FCN8, and Nas-U-Net.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Neural Architecture Search (NAS) has significantly improved the accuracy of image classification and segmentation. However, these methods concentrate on finding segmentation structures for natural or medical applications. In this study, we introduce a NAS approach based on gradient optimization to identify ideal cell designs for road segmentation. To the best of our knowledge, this work represents the first application of gradient-based NAS to road extraction. Taking insight from the U-Net model and its successful variations in different image segmentation tasks, we propose NAS-enhanced U-Net, illustrated by an equal number of cells in both encoder and decoder levels. While cross-entropy combined with dice loss is commonly used in many segmentation tasks, road extraction brings up a unique challenge due to class imbalance. To address this, we introduce a combination of loss function. This function merges cross-entropy with weighted Dice loss, focusing on elevating the importance of the road class by assigning it a weight (⍵), while background Dice values are disregarded. The results indicate that the optimal weight for the proposed model equals 2. Additionally, our work challenges the assumption that increased model parameters or depth inherently leads to improved performance. Therefore, we establish search spaces 2,3,4,5,6,7 and 8 to automatically choose the optimal depth for model. We present promising segmentation results for our proposed method, achieved without any pretraining on the Massachusetts road dataset. Furthermore, these results are compared with those of 14 models categorized into four groups: U-Net, Segnet, FCN8, and Nas-U-Net.
114.
Xie, Lunchen; Lomurno, Eugenio; Gambella, Matteo; Ardagna, Danilo; Roveri, Manuel; Matteucci, Matteo; Shi, Qingjiang
A Lightweight Neural Architecture Search Model for Medical Image Classification Technical Report
2024.
@techreport{xie2024lightweight,
title = {A Lightweight Neural Architecture Search Model for Medical Image Classification},
author = {Lunchen Xie and Eugenio Lomurno and Matteo Gambella and Danilo Ardagna and Manuel Roveri and Matteo Matteucci and Qingjiang Shi},
url = {https://arxiv.org/abs/2405.03462},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
113.
Berezsky, Oleh; Liashchynskyi, Petro; Pitsun, Oleh; Izonin, Ivan
Synthesis of Convolutional Neural Network architectures for biomedical image classification Journal Article
In: Biomedical Signal Processing and Control, vol. 95, pp. 106325, 2024, ISSN: 1746-8094.
@article{BEREZSKY2024106325,
title = {Synthesis of Convolutional Neural Network architectures for biomedical image classification},
author = {Oleh Berezsky and Petro Liashchynskyi and Oleh Pitsun and Ivan Izonin},
url = {https://www.sciencedirect.com/science/article/pii/S1746809424003835},
doi = {https://doi.org/10.1016/j.bspc.2024.106325},
issn = {1746-8094},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Biomedical Signal Processing and Control},
volume = {95},
pages = {106325},
abstract = {Convolutional Neural Networks (CNNs) are frequently used for image classification. This is crucial for the biomedical image classification used for automatic diagnosis in oncology. Designing optimal convolutional neural network architectures is a routine procedure that requires expert knowledge of computer vision and biomedical image features. To address this issue, we developed an automatic method for finding optimal CNN architectures. Our two-step method includes a genetic algorithm-based micro- and macro-search. Micro-search aims to find the optimal cell architecture based on the number of nodes and a set of predefined operations between nodes. Macro-search identifies the optimal number of cells and the operations between them to obtain the final optimal architecture. We obtained several optimal CNN architectures using the developed method of automatic architecture search. We conducted several computer experiments using cytological image classification as an example. The studies’ findings demonstrated that cytological image classification accuracy is higher compared to the classification accuracy of known CNN architectures (VGG-16, AlexNet, LeNet-5, ResNet-18, ResNet-50, MobileNetV3). The method is efficient because the search time for optimal architectures is short. Additionally, the method of optimal architecture search can be used for the synthesis of architectures used for the classification of other classes of biomedical images.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Convolutional Neural Networks (CNNs) are frequently used for image classification. This is crucial for the biomedical image classification used for automatic diagnosis in oncology. Designing optimal convolutional neural network architectures is a routine procedure that requires expert knowledge of computer vision and biomedical image features. To address this issue, we developed an automatic method for finding optimal CNN architectures. Our two-step method includes a genetic algorithm-based micro- and macro-search. Micro-search aims to find the optimal cell architecture based on the number of nodes and a set of predefined operations between nodes. Macro-search identifies the optimal number of cells and the operations between them to obtain the final optimal architecture. We obtained several optimal CNN architectures using the developed method of automatic architecture search. We conducted several computer experiments using cytological image classification as an example. The studies’ findings demonstrated that cytological image classification accuracy is higher compared to the classification accuracy of known CNN architectures (VGG-16, AlexNet, LeNet-5, ResNet-18, ResNet-50, MobileNetV3). The method is efficient because the search time for optimal architectures is short. Additionally, the method of optimal architecture search can be used for the synthesis of architectures used for the classification of other classes of biomedical images.
112.
Ali, Muhammad Junaid; Moalic, Laurent; Essaid, Mokhtar; Idoumghar, Lhassane
Robust Neural Architecture Search Using Differential Evolution for Medical Images Proceedings Article
In: Smith, Stephen; Correia, João; Cintrano, Christian (Ed.): Applications of Evolutionary Computation, pp. 163–179, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-56855-8.
@inproceedings{10.1007/978-3-031-56855-8_10,
title = {Robust Neural Architecture Search Using Differential Evolution for Medical Images},
author = {Muhammad Junaid Ali and Laurent Moalic and Mokhtar Essaid and Lhassane Idoumghar},
editor = {Stephen Smith and João Correia and Christian Cintrano},
url = {https://link.springer.com/chapter/10.1007/978-3-031-56855-8_10},
isbn = {978-3-031-56855-8},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Applications of Evolutionary Computation},
pages = {163–179},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Recent studies have demonstrated that Convolutional Neural Network (CNN) architectures are sensitive to adversarial attacks with imperceptible permutations. Adversarial attacks on medical images may cause manipulated decisions and decrease the performance of the diagnosis system. The robustness of medical systems is crucial, as it assures an improved healthcare system and assists medical professionals in making decisions. Various studies have been proposed to secure medical systems against adversarial attacks, but they have used handcrafted architectures. This study proposes an evolutionary Neural Architecture Search (NAS) approach for searching robust architectures for medical image classification. The Differential Evolution (DE) algorithm is used as a search algorithm. Furthermore, we utilize an attention-based search space consisting of five different attention layers and sixteen convolution and pooling operations. Experiments on multiple MedMNIST datasets show that the proposed approach has achieved better results than deep learning architectures and a robust NAS approach.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Recent studies have demonstrated that Convolutional Neural Network (CNN) architectures are sensitive to adversarial attacks with imperceptible permutations. Adversarial attacks on medical images may cause manipulated decisions and decrease the performance of the diagnosis system. The robustness of medical systems is crucial, as it assures an improved healthcare system and assists medical professionals in making decisions. Various studies have been proposed to secure medical systems against adversarial attacks, but they have used handcrafted architectures. This study proposes an evolutionary Neural Architecture Search (NAS) approach for searching robust architectures for medical image classification. The Differential Evolution (DE) algorithm is used as a search algorithm. Furthermore, we utilize an attention-based search space consisting of five different attention layers and sixteen convolution and pooling operations. Experiments on multiple MedMNIST datasets show that the proposed approach has achieved better results than deep learning architectures and a robust NAS approach.
111.
Liu, Xin; Tian, Jie; Duan, Peiyong; Yu, Qian; Wang, Gaige; Wang, Yingjie
GrMoNAS: A granularity-based multi-objective NAS framework for efficient medical diagnosis Journal Article
In: Computers in Biology and Medicine, vol. 171, pp. 108118, 2024, ISSN: 0010-4825.
@article{LIU2024108118,
title = {GrMoNAS: A granularity-based multi-objective NAS framework for efficient medical diagnosis},
author = {Xin Liu and Jie Tian and Peiyong Duan and Qian Yu and Gaige Wang and Yingjie Wang},
url = {https://www.sciencedirect.com/science/article/pii/S0010482524002026},
doi = {https://doi.org/10.1016/j.compbiomed.2024.108118},
issn = {0010-4825},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Computers in Biology and Medicine},
volume = {171},
pages = {108118},
abstract = {Neural Architecture Search (NAS) has been widely applied to automate medical image diagnostics. However, traditional NAS methods require significant computational resources and time for performance evaluation. To address this, we introduce the GrMoNAS framework, designed to balance diagnostic accuracy and efficiency using proxy datasets for granularity transformation and multi-objective optimization algorithms. The approach initiates with a coarse granularity phase, wherein diverse candidate neural architectures undergo evaluation utilizing a reduced proxy dataset. This initial phase facilitates the swift and effective identification of architectures exhibiting promise. Subsequently, in the fine granularity phase, a comprehensive validation and optimization process is undertaken for these identified architectures. Concurrently, employing multi-objective optimization and Pareto frontier sorting aims to enhance both accuracy and computational efficiency simultaneously. Importantly, the GrMoNAS framework is particularly suitable for hospitals with limited computational resources. We evaluated GrMoNAS in a range of medical scenarios, such as COVID-19, Skin cancer, Lung, Colon, and Acute Lymphoblastic Leukemia diseases, comparing it against traditional models like VGG16, VGG19, and recent NAS approaches including GA-CNN, EBNAS, NEXception, and CovNAS. The results show that GrMoNAS achieves comparable or superior diagnostic precision, significantly enhancing diagnostic efficiency. Moreover, GrMoNAS effectively avoids local optima, indicating its significant potential for precision medical diagnosis.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Neural Architecture Search (NAS) has been widely applied to automate medical image diagnostics. However, traditional NAS methods require significant computational resources and time for performance evaluation. To address this, we introduce the GrMoNAS framework, designed to balance diagnostic accuracy and efficiency using proxy datasets for granularity transformation and multi-objective optimization algorithms. The approach initiates with a coarse granularity phase, wherein diverse candidate neural architectures undergo evaluation utilizing a reduced proxy dataset. This initial phase facilitates the swift and effective identification of architectures exhibiting promise. Subsequently, in the fine granularity phase, a comprehensive validation and optimization process is undertaken for these identified architectures. Concurrently, employing multi-objective optimization and Pareto frontier sorting aims to enhance both accuracy and computational efficiency simultaneously. Importantly, the GrMoNAS framework is particularly suitable for hospitals with limited computational resources. We evaluated GrMoNAS in a range of medical scenarios, such as COVID-19, Skin cancer, Lung, Colon, and Acute Lymphoblastic Leukemia diseases, comparing it against traditional models like VGG16, VGG19, and recent NAS approaches including GA-CNN, EBNAS, NEXception, and CovNAS. The results show that GrMoNAS achieves comparable or superior diagnostic precision, significantly enhancing diagnostic efficiency. Moreover, GrMoNAS effectively avoids local optima, indicating its significant potential for precision medical diagnosis.
110.
Cui, Suhan; Wang, Jiaqi; Zhong, Yuan; Liu, Han; Wang, Ting; Ma, Fenglong
Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions Technical Report
2024.
@techreport{cui2024automated,
title = {Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions},
author = {Suhan Cui and Jiaqi Wang and Yuan Zhong and Han Liu and Ting Wang and Fenglong Ma},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
109.
Fuentes-Tomás, José Antonio; Acosta-Mesa, Héctor Gabriel; Mezura-Montes, Efrén; Jiménez, Rodolfo Hernandez
Neural Architecture Search for Placenta Segmentation in 2D Ultrasound Images Proceedings Article
In: Calvo, Hiram; Martínez-Villaseñor, Lourdes; Ponce, Hiram; Cabada, Ramón Zatarain; Rivera, Martín Montes; Mezura-Montes, Efrén (Ed.): Advances in Computational Intelligence. MICAI 2023 International Workshops, pp. 397–408, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-51940-6.
@inproceedings{10.1007/978-3-031-51940-6_30,
title = {Neural Architecture Search for Placenta Segmentation in 2D Ultrasound Images},
author = {José Antonio Fuentes-Tomás and Héctor Gabriel Acosta-Mesa and Efrén Mezura-Montes and Rodolfo Hernandez Jiménez},
editor = {Hiram Calvo and Lourdes Martínez-Villaseñor and Hiram Ponce and Ramón Zatarain Cabada and Martín Montes Rivera and Efrén Mezura-Montes},
url = {https://link.springer.com/chapter/10.1007/978-3-031-51940-6_30#citeas},
isbn = {978-3-031-51940-6},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Advances in Computational Intelligence. MICAI 2023 International Workshops},
pages = {397–408},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Monitoring the placenta during pregnancy can lead to early diagnosis of anomalies by observing their characteristics, such as size, shape, and location. Ultrasound is a popular medical imaging technique used in placenta monitoring, whose advantages include the non-invasive feature, price, and accessibility. However, images from this domain are characterized by their noise. A segmentation system is required to recognize placenta features. U-Net architecture is a convolutional neural network that has become popular in the literature for medical image segmentation tasks. However, this type is a general-purpose network that requires great expertise to design and may only be applicable in some domains. The evolutionary computation overcomes this limitation, leading to the automatic design of convolutional neural networks. This work proposes a U-Net-based neural architecture search algorithm to construct convolutional neural networks applied in the placenta segmentation on 2D ultrasound images. The results show that the proposed algorithm allows a decrease in the number of parameters of U-Net, ranging from 80 to 98%. Moreover, the segmentation performance achieves a competitive level compared to U-Net, with a difference of 0.012 units in the Dice index.},
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Monitoring the placenta during pregnancy can lead to early diagnosis of anomalies by observing their characteristics, such as size, shape, and location. Ultrasound is a popular medical imaging technique used in placenta monitoring, whose advantages include the non-invasive feature, price, and accessibility. However, images from this domain are characterized by their noise. A segmentation system is required to recognize placenta features. U-Net architecture is a convolutional neural network that has become popular in the literature for medical image segmentation tasks. However, this type is a general-purpose network that requires great expertise to design and may only be applicable in some domains. The evolutionary computation overcomes this limitation, leading to the automatic design of convolutional neural networks. This work proposes a U-Net-based neural architecture search algorithm to construct convolutional neural networks applied in the placenta segmentation on 2D ultrasound images. The results show that the proposed algorithm allows a decrease in the number of parameters of U-Net, ranging from 80 to 98%. Moreover, the segmentation performance achieves a competitive level compared to U-Net, with a difference of 0.012 units in the Dice index.
108.
Liu, Jianwei Zhao Jie Li Xin
Evolutionary Neural Architecture Search and Its Applications in Healthcare Journal Article
In: Computer Modeling in Engineering & Sciences, vol. 139, no. 1, pp. 143–185, 2024, ISSN: 1526-1506.
@article{cmes.2023.030391,
title = {Evolutionary Neural Architecture Search and Its Applications in Healthcare},
author = {Jianwei Zhao Jie Li Xin Liu},
url = {http://www.techscience.com/CMES/v139n1/55101},
doi = {10.32604/cmes.2023.030391},
issn = {1526-1506},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Computer Modeling in Engineering & Sciences},
volume = {139},
number = {1},
pages = {143–185},
abstract = {Most of the neural network architectures are based on human experience, which requires a long and tedious trial-and-error process. Neural architecture search (NAS) attempts to detect effective architectures without human intervention. Evolutionary algorithms (EAs) for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures. Using multiobjective EAs for NAS, optimal neural architectures that meet various performance criteria can be explored and discovered efficiently. Furthermore, hardware-accelerated NAS methods can improve the efficiency of the NAS. While existing reviews have mainly focused on different strategies to complete NAS, a few studies have explored the use of EAs for NAS. In this paper, we summarize and explore the use of EAs for NAS, as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods. NAS performs well in healthcare applications, such as medical image analysis, classification of disease diagnosis, and health monitoring. EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task. Deep neural network has been successfully used in healthcare, but it lacks interpretability. Medical data is highly sensitive, and privacy leaks are frequently reported in the healthcare industry. To solve these problems, in healthcare, we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection. Moreover, we also point out future research directions for evolutionary NAS. Overall, for researchers who want to use EAs to optimize NNs in healthcare, we analyze the advantages and disadvantages of doing so to provide detailed guidance, and propose an interpretable privacy-preserving framework for healthcare applications.},
keywords = {},
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Most of the neural network architectures are based on human experience, which requires a long and tedious trial-and-error process. Neural architecture search (NAS) attempts to detect effective architectures without human intervention. Evolutionary algorithms (EAs) for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures. Using multiobjective EAs for NAS, optimal neural architectures that meet various performance criteria can be explored and discovered efficiently. Furthermore, hardware-accelerated NAS methods can improve the efficiency of the NAS. While existing reviews have mainly focused on different strategies to complete NAS, a few studies have explored the use of EAs for NAS. In this paper, we summarize and explore the use of EAs for NAS, as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods. NAS performs well in healthcare applications, such as medical image analysis, classification of disease diagnosis, and health monitoring. EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task. Deep neural network has been successfully used in healthcare, but it lacks interpretability. Medical data is highly sensitive, and privacy leaks are frequently reported in the healthcare industry. To solve these problems, in healthcare, we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection. Moreover, we also point out future research directions for evolutionary NAS. Overall, for researchers who want to use EAs to optimize NNs in healthcare, we analyze the advantages and disadvantages of doing so to provide detailed guidance, and propose an interpretable privacy-preserving framework for healthcare applications.
107.
Yang, Dong; Roth, Holger R.; Wang, Xiaosong; Xu, Ziyue; Xu, Daguang
In: Zhou, S. Kevin; Greenspan, Hayit; Shen, Dinggang (Ed.): Deep Learning for Medical Image Analysis (Second Edition), pp. 281-298, Academic Press, 2024, ISBN: 978-0-323-85124-4.
@incollection{YANG2024281,
title = {Chapter 10 - Dynamic inference using neural architecture search in medical image segmentation: From a novel adaptation perspective},
author = {Dong Yang and Holger R. Roth and Xiaosong Wang and Ziyue Xu and Daguang Xu},
editor = {S. Kevin Zhou and Hayit Greenspan and Dinggang Shen},
url = {https://www.sciencedirect.com/science/article/pii/B9780323851244000210},
doi = {https://doi.org/10.1016/B978-0-32-385124-4.00021-0},
isbn = {978-0-323-85124-4},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Deep Learning for Medical Image Analysis (Second Edition)},
pages = {281-298},
publisher = {Academic Press},
edition = {Second Edition},
series = {The MICCAI Society book Series},
abstract = {Data inconsistency in medical imaging acquisition has been existing for decades, which creates difficulties when researchers adopt learning-based processing methods to unknown data. This issue is mostly caused by medical image scanners from different vendors, inconsistent scanning protocols, anatomy discrepancy among populations, environmental artifacts or other related factors. For instance, large appearance variance may exist in 3D T2-weighted brain MRI from different institutions or hospitals, even scanned with the same scanning protocols. Meanwhile, the data inconsistency downgrades the performance of machine learning models for medical image processing, such as organ or tumor segmentation, when models face unknown data at inference with pre-trained models. To alleviate the potential side effects caused by the data inconsistency, we propose a novel approach to improve model generalizability and transferability for unknown data leveraging the concepts from neural architecture search. We build a general “super-net” enabling multiple candidate modules in parallel to represent multi-scale contextual features at different network levels, respectively. After the training of the super-net is accomplished, a unique and optimal architecture for each data point is determined with guidance of additional model constraints at inference. We also propose a novel path sampling strategy to enable “fair” model training. Our experiments show that the proposed approach has clear advantages over the conventional neural network deployment in terms of segmentation performance and generalization in the unknown images.},
keywords = {},
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Data inconsistency in medical imaging acquisition has been existing for decades, which creates difficulties when researchers adopt learning-based processing methods to unknown data. This issue is mostly caused by medical image scanners from different vendors, inconsistent scanning protocols, anatomy discrepancy among populations, environmental artifacts or other related factors. For instance, large appearance variance may exist in 3D T2-weighted brain MRI from different institutions or hospitals, even scanned with the same scanning protocols. Meanwhile, the data inconsistency downgrades the performance of machine learning models for medical image processing, such as organ or tumor segmentation, when models face unknown data at inference with pre-trained models. To alleviate the potential side effects caused by the data inconsistency, we propose a novel approach to improve model generalizability and transferability for unknown data leveraging the concepts from neural architecture search. We build a general “super-net” enabling multiple candidate modules in parallel to represent multi-scale contextual features at different network levels, respectively. After the training of the super-net is accomplished, a unique and optimal architecture for each data point is determined with guidance of additional model constraints at inference. We also propose a novel path sampling strategy to enable “fair” model training. Our experiments show that the proposed approach has clear advantages over the conventional neural network deployment in terms of segmentation performance and generalization in the unknown images.
106.
Fuentes-Tomás, José-Antonio; Mezura-Montes, Efrén; Acosta-Mesa, Héctor-Gabriel; Márquez-Grajales, Aldo
Tree-Based Codification in Neural Architecture Search for Medical Image Segmentation Journal Article
In: IEEE Transactions on Evolutionary Computation, pp. 1-1, 2024.
@article{10391062,
title = {Tree-Based Codification in Neural Architecture Search for Medical Image Segmentation},
author = {José-Antonio Fuentes-Tomás and Efrén Mezura-Montes and Héctor-Gabriel Acosta-Mesa and Aldo Márquez-Grajales},
url = {https://ieeexplore.ieee.org/abstract/document/10391062},
doi = {10.1109/TEVC.2024.3353182},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Evolutionary Computation},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
105.
Wang, Yan; Zhen, Liangli; Zhang, Jianwei; Li, Miqing; Zhang, Lei; Wang, Zizhou; Feng, Yangqin; Xue, Yu; Wang, Xiao; Chen, Zheng; Luo, Tao; Goh, Rich Siow Mong; Liu, Yong
MedNAS: Multi-Scale Training-Free Neural Architecture Search for Medical Image Analysis Journal Article
In: IEEE Transactions on Evolutionary Computation, pp. 1-1, 2024.
@article{10391077,
title = {MedNAS: Multi-Scale Training-Free Neural Architecture Search for Medical Image Analysis},
author = {Yan Wang and Liangli Zhen and Jianwei Zhang and Miqing Li and Lei Zhang and Zizhou Wang and Yangqin Feng and Yu Xue and Xiao Wang and Zheng Chen and Tao Luo and Rich Siow Mong Goh and Yong Liu},
url = {https://ieeexplore.ieee.org/abstract/document/10391077/authors#authors},
doi = {10.1109/TEVC.2024.3352641},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Evolutionary Computation},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
104.
Şahin, Emrullah; Özdemir, Durmuş; Temurtaş, Hasan
Multi-objective optimization of ViT architecture for efficient brain tumor classification Journal Article
In: Biomedical Signal Processing and Control, vol. 91, pp. 105938, 2024, ISSN: 1746-8094.
@article{SAHIN2024105938,
title = {Multi-objective optimization of ViT architecture for efficient brain tumor classification},
author = {Emrullah Şahin and Durmuş Özdemir and Hasan Temurtaş},
url = {https://www.sciencedirect.com/science/article/pii/S174680942301371X},
doi = {https://doi.org/10.1016/j.bspc.2023.105938},
issn = {1746-8094},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Biomedical Signal Processing and Control},
volume = {91},
pages = {105938},
abstract = {This study presents an advanced approach to optimizing the Vision Transformer (ViT) network for brain tumor classification in 2D MRI images, utilizing Bayesian Multi-Objective (BMO) optimization techniques. Rather than merely addressing the limitations of the standard ViT model, our objective was to enhance its overall efficiency and effectiveness. The application of BMO enabled us to fine-tune the architectural parameters of the ViT network, resulting in a model that was not only twice as fast but also four times smaller in size compared to the original. In terms of performance, the optimized ViT model achieved notable improvements, with a 1.48 % increase in validation accuracy, a 3.23 % rise in the F1-score, and a 3.36 % improvement in precision. These substantial enhancements highlight the potential of integrating BMO with visual transformer-based models, suggesting a promising direction for future research in achieving high efficiency and accuracy in complex classification tasks.},
keywords = {},
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This study presents an advanced approach to optimizing the Vision Transformer (ViT) network for brain tumor classification in 2D MRI images, utilizing Bayesian Multi-Objective (BMO) optimization techniques. Rather than merely addressing the limitations of the standard ViT model, our objective was to enhance its overall efficiency and effectiveness. The application of BMO enabled us to fine-tune the architectural parameters of the ViT network, resulting in a model that was not only twice as fast but also four times smaller in size compared to the original. In terms of performance, the optimized ViT model achieved notable improvements, with a 1.48 % increase in validation accuracy, a 3.23 % rise in the F1-score, and a 3.36 % improvement in precision. These substantial enhancements highlight the potential of integrating BMO with visual transformer-based models, suggesting a promising direction for future research in achieving high efficiency and accuracy in complex classification tasks.
103.
Gao, Jianliang; Wu, Zhenpeng; Al-Sabri, Raeed; Oloulade, Babatounde Moctard; Chen, Jiamin
AutoDDI: Drug–Drug Interaction Prediction With Automated Graph Neural Network Journal Article
In: IEEE Journal of Biomedical and Health Informatics, pp. 1-12, 2024.
@article{10380606,
title = {AutoDDI: Drug–Drug Interaction Prediction With Automated Graph Neural Network},
author = {Jianliang Gao and Zhenpeng Wu and Raeed Al-Sabri and Babatounde Moctard Oloulade and Jiamin Chen},
doi = {10.1109/JBHI.2024.3349570},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {1-12},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2023
102.
He, Xin; Chu, Xiaowen
MedPipe: End-to-End Joint Search of Data Augmentation and Neural Architecture for 3D Medical Image Classification Journal Article
In: 2023.
@article{He_2023,
title = {MedPipe: End-to-End Joint Search of Data Augmentation and Neural Architecture for 3D Medical Image Classification},
author = {Xin He and Xiaowen Chu},
url = {http://dx.doi.org/10.36227/techrxiv.19513780.v2},
doi = {10.36227/techrxiv.19513780.v2},
year = {2023},
date = {2023-11-01},
urldate = {2023-11-01},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
101.
Akinola, Solomon Oluwole; Qingguo, Wang; Olukanmi, Peter; Tshilidzi, Marwala
A Boosted Evolutionary Neural Architecture Search for Timeseries Forecasting with Application to South African COVID-19 Cases Journal Article
In: International Journal of Online and Biomedical Engineering (iJOE), vol. 19, no. 14, pp. pp. 107–130, 2023.
@article{Akinola_Qingguo_Olukanmi_Tshilidzi_2023,
title = {A Boosted Evolutionary Neural Architecture Search for Timeseries Forecasting with Application to South African COVID-19 Cases},
author = {Solomon Oluwole Akinola and Wang Qingguo and Peter Olukanmi and Marwala Tshilidzi},
url = {https://online-journals.org/index.php/i-joe/article/view/41291},
doi = {10.3991/ijoe.v19i14.41291},
year = {2023},
date = {2023-10-01},
urldate = {2023-10-01},
journal = {International Journal of Online and Biomedical Engineering (iJOE)},
volume = {19},
number = {14},
pages = {pp. 107–130},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
100.
Zhang, Tian; Li, Nan; Zhou, Yuee; Cai, Wei; Ma, Lianbo
Information extraction of Chinese medical electronic records via evolutionary neural architecture search Proceedings Article
In: 2023 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 396-405, 2023.
@inproceedings{10411614,
title = {Information extraction of Chinese medical electronic records via evolutionary neural architecture search},
author = {Tian Zhang and Nan Li and Yuee Zhou and Wei Cai and Lianbo Ma},
url = {https://ieeexplore.ieee.org/abstract/document/10411614},
doi = {10.1109/ICDMW60847.2023.00056},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2023 IEEE International Conference on Data Mining Workshops (ICDMW)},
pages = {396-405},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
99.
Meng, Lingtong; Chen, Yuting
DFairNAS: A Dataflow Fairness Approach to Training NAS Neural Networks Proceedings Article
In: 2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1-6, 2023.
@inproceedings{10373372,
title = {DFairNAS: A Dataflow Fairness Approach to Training NAS Neural Networks},
author = {Lingtong Meng and Yuting Chen},
url = {https://ieeexplore.ieee.org/abstract/document/10373372},
doi = {10.1109/CISP-BMEI60920.2023.10373372},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)},
pages = {1-6},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
98.
Mu, Pan; Wu, Guanyao; Liu, Jinyuan; Zhang, Yuduo; Fan, Xin; Liu, Risheng
Learning to Search a Lightweight Generalized Network for Medical Image Fusion Journal Article
In: IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1, 2023.
@article{10360160,
title = {Learning to Search a Lightweight Generalized Network for Medical Image Fusion},
author = {Pan Mu and Guanyao Wu and Jinyuan Liu and Yuduo Zhang and Xin Fan and Risheng Liu},
url = {https://ieeexplore.ieee.org/abstract/document/10360160},
doi = {10.1109/TCSVT.2023.3342808},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {IEEE Transactions on Circuits and Systems for Video Technology},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
97.
Li, J.; Lv, Y.; Zhang, P.; Zhao, J.
Neural Architecture Search for Unsupervised PET Image Denoising Proceedings Article
In: 2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD), pp. 1-1, 2023.
@inproceedings{10338097,
title = {Neural Architecture Search for Unsupervised PET Image Denoising},
author = {J. Li and Y. Lv and P. Zhang and J. Zhao},
doi = {10.1109/NSSMICRTSD49126.2023.10338097},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD)},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
96.
Yang, Changdi; Sheng, Yi; Dong, Peiyan; Kong, Zhenglun; Li, Yanyu; Yu, Pinrui; Yang, Lei; Lin, Xue; Wang, Yanzhi
Fast and Fair Medical AI on the Edge Through Neural Architecture Search for Hybrid Vision Models Proceedings Article
In: 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD), pp. 01-09, 2023.
@inproceedings{10323652,
title = {Fast and Fair Medical AI on the Edge Through Neural Architecture Search for Hybrid Vision Models},
author = {Changdi Yang and Yi Sheng and Peiyan Dong and Zhenglun Kong and Yanyu Li and Pinrui Yu and Lei Yang and Xue Lin and Yanzhi Wang},
doi = {10.1109/ICCAD57390.2023.10323652},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)},
pages = {01-09},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
95.
Liu, Xiao; Yao, Chong; Chen, Hongyi; Xiang, Rui; Wu, Hao; Du, Peng; Yu, Zekuan; Liu, Weifan; Liu, Jie; Geng, Daoying
BTSC-TNAS: A neural architecture search-based transformer for brain tumor segmentation and classification Journal Article
In: Computerized Medical Imaging and Graphics, vol. 110, pp. 102307, 2023, ISSN: 0895-6111.
@article{LIU2023102307,
title = {BTSC-TNAS: A neural architecture search-based transformer for brain tumor segmentation and classification},
author = {Xiao Liu and Chong Yao and Hongyi Chen and Rui Xiang and Hao Wu and Peng Du and Zekuan Yu and Weifan Liu and Jie Liu and Daoying Geng},
url = {https://www.sciencedirect.com/science/article/pii/S0895611123001258},
doi = {https://doi.org/10.1016/j.compmedimag.2023.102307},
issn = {0895-6111},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Computerized Medical Imaging and Graphics},
volume = {110},
pages = {102307},
abstract = {Glioblastoma (GBM), isolated brain metastasis (SBM), and primary central nervous system lymphoma (PCNSL) possess a high level of similarity in histomorphology and clinical manifestations on multimodal MRI. Such similarities have led to challenges in the clinical diagnosis of these three malignant tumors. However, many existing models solely focus on either the task of segmentation or classification, which limits the application of computer-aided diagnosis in clinical diagnosis and treatment. To solve this problem, we propose a multi-task learning transformer with neural architecture search (NAS) for brain tumor segmentation and classification (BTSC-TNAS). In the segmentation stage, we use a nested transformer U-shape network (NTU-NAS) with NAS to directly predict brain tumor masks from multi-modal MRI images. In the tumor classification stage, we use the multiscale features obtained from the encoder of NTU-NAS as the input features of the classification network (MSC-NET), which are integrated and corrected by the classification feature correction enhancement (CFCE) block to improve the accuracy of classification. The proposed BTSC-TNAS achieves an average Dice coefficient of 80.86% and 87.12% for the segmentation of tumor region and the maximum abnormal region in clinical data respectively. The model achieves a classification accuracy of 0.941. The experiments performed on the BraTS 2019 dataset show that the proposed BTSC-TNAS has excellent generalizability and can provide support for some challenging tasks in the diagnosis and treatment of brain tumors.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Glioblastoma (GBM), isolated brain metastasis (SBM), and primary central nervous system lymphoma (PCNSL) possess a high level of similarity in histomorphology and clinical manifestations on multimodal MRI. Such similarities have led to challenges in the clinical diagnosis of these three malignant tumors. However, many existing models solely focus on either the task of segmentation or classification, which limits the application of computer-aided diagnosis in clinical diagnosis and treatment. To solve this problem, we propose a multi-task learning transformer with neural architecture search (NAS) for brain tumor segmentation and classification (BTSC-TNAS). In the segmentation stage, we use a nested transformer U-shape network (NTU-NAS) with NAS to directly predict brain tumor masks from multi-modal MRI images. In the tumor classification stage, we use the multiscale features obtained from the encoder of NTU-NAS as the input features of the classification network (MSC-NET), which are integrated and corrected by the classification feature correction enhancement (CFCE) block to improve the accuracy of classification. The proposed BTSC-TNAS achieves an average Dice coefficient of 80.86% and 87.12% for the segmentation of tumor region and the maximum abnormal region in clinical data respectively. The model achieves a classification accuracy of 0.941. The experiments performed on the BraTS 2019 dataset show that the proposed BTSC-TNAS has excellent generalizability and can provide support for some challenging tasks in the diagnosis and treatment of brain tumors.
94.
Yu, Caiyang; Wang, Yixi; Tang, Chenwei; Feng, Wentao; Lv, Jiancheng
EU-Net: Automatic U-Net neural architecture search with differential evolutionary algorithm for medical image segmentation Journal Article
In: Computers in Biology and Medicine, vol. 167, pp. 107579, 2023, ISSN: 0010-4825.
@article{YU2023107579,
title = {EU-Net: Automatic U-Net neural architecture search with differential evolutionary algorithm for medical image segmentation},
author = {Caiyang Yu and Yixi Wang and Chenwei Tang and Wentao Feng and Jiancheng Lv},
url = {https://www.sciencedirect.com/science/article/pii/S0010482523010442},
doi = {https://doi.org/10.1016/j.compbiomed.2023.107579},
issn = {0010-4825},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Computers in Biology and Medicine},
volume = {167},
pages = {107579},
abstract = {Medical images are crucial in clinical practice, providing essential information for patient assessment and treatment planning. However, manual extraction of information from images is both time-consuming and prone to errors. The emergence of U-Net addresses this challenge by automating the segmentation of anatomical structures and pathological lesions in medical images, thereby significantly enhancing the accuracy of image interpretation and diagnosis. However, the performance of U-Net largely depends on its encoder–decoder structure, which requires researchers with knowledge of neural network architecture design and an in-depth understanding of medical images. In this paper, we propose an automatic U-Net Neural Architecture Search (NAS) algorithm using the differential evolutionary (DE) algorithm, named EU-Net, to segment critical information in medical images to assist physicians in diagnosis. Specifically, by presenting the variable-length strategy, the proposed EU-Net algorithm can sufficiently and automatically search for the neural network architecture without expertise. Moreover, the utilization of crossover, mutation, and selection strategies of DE takes account of the trade-off between exploration and exploitation in the search space. Finally, in the encoding and decoding phases of the proposed algorithm, different block-based and layer-based structures are introduced for architectural optimization. The proposed EU-Net algorithm is validated on two widely used medical datasets, i.e., CHAOS and BUSI, for image segmentation tasks. Extensive experimental results show that the proposed EU-Net algorithm outperforms the chosen peer competitors in both two datasets. In particular, compared to the original U-Net, our proposed method improves the metric mIou by at least 6%.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Medical images are crucial in clinical practice, providing essential information for patient assessment and treatment planning. However, manual extraction of information from images is both time-consuming and prone to errors. The emergence of U-Net addresses this challenge by automating the segmentation of anatomical structures and pathological lesions in medical images, thereby significantly enhancing the accuracy of image interpretation and diagnosis. However, the performance of U-Net largely depends on its encoder–decoder structure, which requires researchers with knowledge of neural network architecture design and an in-depth understanding of medical images. In this paper, we propose an automatic U-Net Neural Architecture Search (NAS) algorithm using the differential evolutionary (DE) algorithm, named EU-Net, to segment critical information in medical images to assist physicians in diagnosis. Specifically, by presenting the variable-length strategy, the proposed EU-Net algorithm can sufficiently and automatically search for the neural network architecture without expertise. Moreover, the utilization of crossover, mutation, and selection strategies of DE takes account of the trade-off between exploration and exploitation in the search space. Finally, in the encoding and decoding phases of the proposed algorithm, different block-based and layer-based structures are introduced for architectural optimization. The proposed EU-Net algorithm is validated on two widely used medical datasets, i.e., CHAOS and BUSI, for image segmentation tasks. Extensive experimental results show that the proposed EU-Net algorithm outperforms the chosen peer competitors in both two datasets. In particular, compared to the original U-Net, our proposed method improves the metric mIou by at least 6%.
93.
Ghosh, Arjun; Jana, Nanda Dulal; Das, Swagatam; Mallipeddi, Rammohan
Two-Phase Evolutionary Convolutional Neural Network Architecture Search for Medical Image Classification Journal Article
In: IEEE Access, vol. 11, pp. 115280–115305, 2023.
@article{DBLP:journals/access/GhoshJDM23,
title = {Two-Phase Evolutionary Convolutional Neural Network Architecture Search for Medical Image Classification},
author = {Arjun Ghosh and Nanda Dulal Jana and Swagatam Das and Rammohan Mallipeddi},
url = {https://doi.org/10.1109/ACCESS.2023.3323705},
doi = {10.1109/ACCESS.2023.3323705},
year = {2023},
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92.
Ghosh, Arjun; Jana, Nanda Dulal; Das, Swagatam; Mallipeddi, Rammohan
Two-Phase Evolutionary Convolutional Neural Network Architecture Search for Medical Image Classification Journal Article
In: IEEE Access, vol. 11, pp. 115280-115305, 2023.
@article{10278411,
title = {Two-Phase Evolutionary Convolutional Neural Network Architecture Search for Medical Image Classification},
author = {Arjun Ghosh and Nanda Dulal Jana and Swagatam Das and Rammohan Mallipeddi},
url = {https://ieeexplore.ieee.org/document/10278411},
doi = {10.1109/ACCESS.2023.3323705},
year = {2023},
date = {2023-01-01},
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91.
Xu, Hongyan; Wang, Dadong; Sowmya, Arcot; Katz, Ian
Detection of Basal Cell Carcinoma in Whole Slide Images Proceedings Article
In: Greenspan, Hayit; Madabhushi, Anant; Mousavi, Parvin; Salcudean, Septimiu; Duncan, James; Syeda-Mahmood, Tanveer; Taylor, Russell (Ed.): Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, pp. 263–272, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-43987-2.
@inproceedings{10.1007/978-3-031-43987-2_26,
title = {Detection of Basal Cell Carcinoma in Whole Slide Images},
author = {Hongyan Xu and Dadong Wang and Arcot Sowmya and Ian Katz},
editor = {Hayit Greenspan and Anant Madabhushi and Parvin Mousavi and Septimiu Salcudean and James Duncan and Tanveer Syeda-Mahmood and Russell Taylor},
url = {https://link.springer.com/chapter/10.1007/978-3-031-43987-2_26},
isbn = {978-3-031-43987-2},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Medical Image Computing and Computer Assisted Intervention – MICCAI 2023},
pages = {263–272},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Basal cell carcinoma (BCC) is a prevalent and increasingly diagnosed form of skin cancer that can benefit from automated whole slide image (WSI) analysis. However, traditional methods that utilize popular network structures designed for natural images, such as the ImageNet dataset, may result in reduced accuracy due to the significant differences between natural and pathology images. In this paper, we analyze skin cancer images using the optimal network obtained by neural architecture search (NAS) on the skin cancer dataset. Compared with traditional methods, our network is more applicable to the task of skin cancer detection. Furthermore, unlike traditional unilaterally augmented (UA) methods, the proposed supernet Skin-Cancer net (SC-net) considers the fairness of training and alleviates the effects of evaluation bias. We use the SC-net to fairly treat all the architectures in the search space and leveraged evolutionary search to obtain the optimal architecture for a skin cancer dataset. Our experiments involve 277,000 patches split from 194 slides. Under the same FLOPs budget (4.1G), our searched ResNet50 model achieves 96.2% accuracy and 96.5% area under the ROC curve (AUC), which are 4.8% and 4.7% higher than those with the baseline settings, respectively.},
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Basal cell carcinoma (BCC) is a prevalent and increasingly diagnosed form of skin cancer that can benefit from automated whole slide image (WSI) analysis. However, traditional methods that utilize popular network structures designed for natural images, such as the ImageNet dataset, may result in reduced accuracy due to the significant differences between natural and pathology images. In this paper, we analyze skin cancer images using the optimal network obtained by neural architecture search (NAS) on the skin cancer dataset. Compared with traditional methods, our network is more applicable to the task of skin cancer detection. Furthermore, unlike traditional unilaterally augmented (UA) methods, the proposed supernet Skin-Cancer net (SC-net) considers the fairness of training and alleviates the effects of evaluation bias. We use the SC-net to fairly treat all the architectures in the search space and leveraged evolutionary search to obtain the optimal architecture for a skin cancer dataset. Our experiments involve 277,000 patches split from 194 slides. Under the same FLOPs budget (4.1G), our searched ResNet50 model achieves 96.2% accuracy and 96.5% area under the ROC curve (AUC), which are 4.8% and 4.7% higher than those with the baseline settings, respectively.
90.
Yang, Dong; Xu, Ziyue; He, Yufan; Nath, Vishwesh; Li, Wenqi; Myronenko, Andriy; Hatamizadeh, Ali; Zhao, Can; Roth, Holger R.; Xu, Daguang
DAST: Differentiable Architecture Search with Transformer for 3D Medical Image Segmentation Proceedings Article
In: Greenspan, Hayit; Madabhushi, Anant; Mousavi, Parvin; Salcudean, Septimiu; Duncan, James; Syeda-Mahmood, Tanveer; Taylor, Russell (Ed.): Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, pp. 747–756, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-43898-1.
@inproceedings{10.1007/978-3-031-43898-1_71,
title = {DAST: Differentiable Architecture Search with Transformer for 3D Medical Image Segmentation},
author = {Dong Yang and Ziyue Xu and Yufan He and Vishwesh Nath and Wenqi Li and Andriy Myronenko and Ali Hatamizadeh and Can Zhao and Holger R. Roth and Daguang Xu},
editor = {Hayit Greenspan and Anant Madabhushi and Parvin Mousavi and Septimiu Salcudean and James Duncan and Tanveer Syeda-Mahmood and Russell Taylor},
url = {https://link.springer.com/chapter/10.1007/978-3-031-43898-1_71},
isbn = {978-3-031-43898-1},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Medical Image Computing and Computer Assisted Intervention – MICCAI 2023},
pages = {747–756},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Neural Architecture Search (NAS) has been widely used for medical image segmentation by improving both model performance and computational efficiency. Recently, the Visual Transformer (ViT) model has achieved significant success in computer vision tasks. Leveraging these two innovations, we propose a novel NAS algorithm, DAST, to optimize neural network models with transformers for 3D medical image segmentation. The proposed algorithm is able to search the global structure and local operations of the architecture with a GPU memory consumption constraint. The resulting architectures reveal an effective relationship between convolution and transformer layers in segmentation models. Moreover, we validate the proposed algorithm on large-scale medical image segmentation data sets, showing its superior performance over the baselines. The model achieves state-of-the-art performance in the public challenge of kidney CT segmentation (KiTS'19).},
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Neural Architecture Search (NAS) has been widely used for medical image segmentation by improving both model performance and computational efficiency. Recently, the Visual Transformer (ViT) model has achieved significant success in computer vision tasks. Leveraging these two innovations, we propose a novel NAS algorithm, DAST, to optimize neural network models with transformers for 3D medical image segmentation. The proposed algorithm is able to search the global structure and local operations of the architecture with a GPU memory consumption constraint. The resulting architectures reveal an effective relationship between convolution and transformer layers in segmentation models. Moreover, we validate the proposed algorithm on large-scale medical image segmentation data sets, showing its superior performance over the baselines. The model achieves state-of-the-art performance in the public challenge of kidney CT segmentation (KiTS'19).
89.
Ying, Weiqin; Zheng, Qiaoqiao; Wu, Yu; Yang, Kaihao; Zhou, Zekun; Chen, Jiajun; Ye, Zilin
In: Applied Soft Computing, vol. 148, pp. 110869, 2023, ISSN: 1568-4946.
@article{YING2023110869,
title = {Efficient multi-objective evolutionary neural architecture search for U-Nets with diamond atrous convolution and Transformer for medical image segmentation},
author = {Weiqin Ying and Qiaoqiao Zheng and Yu Wu and Kaihao Yang and Zekun Zhou and Jiajun Chen and Zilin Ye},
url = {https://www.sciencedirect.com/science/article/pii/S1568494623008876},
doi = {https://doi.org/10.1016/j.asoc.2023.110869},
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year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Applied Soft Computing},
volume = {148},
pages = {110869},
abstract = {Deep encoder–decoder neural networks like U-Nets have made significant contributions to the development of computer vision applications such as image segmentation. Neural architecture search (NAS) has the potential to further automatically adjust the architectures of U-Nets for various medical image segmentation tasks. Most of the NAS techniques focus on optimizing segmentation accuracies of network architectures. In real-world medical image segmentation scenarios, two main challenges are poor medical image quality and diverse deployment devices with different computing capabilities. A large architecture designed only for the high segmentation accuracy is difficult to run on various deployment devices. To address these challenges, this paper proposes a multi-objective evolutionary neural architecture search method (CTU-NAS) for U-Nets with diamond atrous convolution and Transformer for medical image segmentation. A hybrid U-Net architecture (CTU-Net) with diamond atrous convolution and Transformer modules is designed as the supernet of CTU-NAS. Then a channel search strategy based on sorting and selection is applied to speed up the search for subnets by precisely selecting and training the most important channels more times. In addition, CTU-NAS employs a dual acceleration mechanism based on weight sharing and surrogate model to lower the cost of evaluations of subnets. CTU-NAS applies a multi-objective evolutionary algorithm to balance between the segmentation accuracy and the number of parameters. Experimental results on two medical segmentation datasets show that CTU-NAS is capable of quickly generating a group of excellent network architectures with different sizes and their performances also outperform or come close to those of the manually designed networks.},
keywords = {},
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Deep encoder–decoder neural networks like U-Nets have made significant contributions to the development of computer vision applications such as image segmentation. Neural architecture search (NAS) has the potential to further automatically adjust the architectures of U-Nets for various medical image segmentation tasks. Most of the NAS techniques focus on optimizing segmentation accuracies of network architectures. In real-world medical image segmentation scenarios, two main challenges are poor medical image quality and diverse deployment devices with different computing capabilities. A large architecture designed only for the high segmentation accuracy is difficult to run on various deployment devices. To address these challenges, this paper proposes a multi-objective evolutionary neural architecture search method (CTU-NAS) for U-Nets with diamond atrous convolution and Transformer for medical image segmentation. A hybrid U-Net architecture (CTU-Net) with diamond atrous convolution and Transformer modules is designed as the supernet of CTU-NAS. Then a channel search strategy based on sorting and selection is applied to speed up the search for subnets by precisely selecting and training the most important channels more times. In addition, CTU-NAS employs a dual acceleration mechanism based on weight sharing and surrogate model to lower the cost of evaluations of subnets. CTU-NAS applies a multi-objective evolutionary algorithm to balance between the segmentation accuracy and the number of parameters. Experimental results on two medical segmentation datasets show that CTU-NAS is capable of quickly generating a group of excellent network architectures with different sizes and their performances also outperform or come close to those of the manually designed networks.
88.
Kuş, Zeki; Kiraz, Berna
Evolutionary Architecture Optimization for Retinal Vessel Segmentation Journal Article
In: IEEE Journal of Biomedical and Health Informatics, pp. 1-9, 2023.
@article{10250938,
title = {Evolutionary Architecture Optimization for Retinal Vessel Segmentation},
author = {Zeki Kuş and Berna Kiraz},
url = {https://ieeexplore.ieee.org/abstract/document/10250938},
doi = {10.1109/JBHI.2023.3314981},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {1-9},
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pubstate = {published},
tppubtype = {article}
}