Do Sharpness-Based Optimizers Improve Generalization in Medical Image Analysis?
Effective clinical deployment of deep learning models in healthcare demands high generalization performance to ensure accurate diagnosis and treatment planning. In recent years, significant research has focused on improving the generalization of deep learning models by regularizing the sharpness of...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10994767/ |
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| author | Mohamed Hassan Aleksandar Vakanski Boyu Zhang Min Xian |
| author_facet | Mohamed Hassan Aleksandar Vakanski Boyu Zhang Min Xian |
| author_sort | Mohamed Hassan |
| collection | DOAJ |
| description | Effective clinical deployment of deep learning models in healthcare demands high generalization performance to ensure accurate diagnosis and treatment planning. In recent years, significant research has focused on improving the generalization of deep learning models by regularizing the sharpness of the loss landscape. Among the optimization approaches that explicitly minimize sharpness, Sharpness-Aware Minimization (SAM) has shown potential in enhancing generalization performance on general domain image datasets. This success has led to the development of several advanced sharpness-based algorithms aimed at addressing the limitations of SAM, such as Adaptive SAM, Surrogate-Gap SAM, Weighted SAM, and Curvature Regularized SAM. These sharpness-based optimizers have shown improvements in model generalization compared to conventional stochastic gradient descent optimizers and their variants on general domain image datasets, but they have not been thoroughly evaluated on medical images. This work provides a review of recent sharpness-based methods for improving the generalization of deep learning networks and evaluates the methods’ performance on three medical image datasets, including breast ultrasound, chest X-ray, and colon histopathology images. Our findings indicate that the initial SAM method successfully enhances the generalization of various deep learning models. While Adaptive SAM improves generalization of convolutional neural networks, it fails to do so for vision transformers. Other sharpness-based optimizers, however, do not demonstrate consistent results. The results reveal that contrary to findings in the non-medical domain, SAM is the only recommended sharpness-based optimizer that consistently improves generalization in medical image analysis, and further research is necessary to refine the variants of SAM to enhance generalization performance in this field. |
| format | Article |
| id | doaj-art-c50fc13acdae4aa5872f0bbbcad407a0 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-c50fc13acdae4aa5872f0bbbcad407a02025-08-20T02:31:44ZengIEEEIEEE Access2169-35362025-01-0113829728298510.1109/ACCESS.2025.356864110994767Do Sharpness-Based Optimizers Improve Generalization in Medical Image Analysis?Mohamed Hassan0https://orcid.org/0009-0005-6934-1385Aleksandar Vakanski1https://orcid.org/0000-0003-3365-1291Boyu Zhang2https://orcid.org/0000-0002-9401-6163Min Xian3https://orcid.org/0000-0001-6098-4441Department of Computer Science, University of Idaho, Idaho Falls, ID, USADepartment of Computer Science, University of Idaho, Idaho Falls, ID, USADepartment of Computer Science, University of Idaho, Idaho Falls, ID, USADepartment of Computer Science, University of Idaho, Idaho Falls, ID, USAEffective clinical deployment of deep learning models in healthcare demands high generalization performance to ensure accurate diagnosis and treatment planning. In recent years, significant research has focused on improving the generalization of deep learning models by regularizing the sharpness of the loss landscape. Among the optimization approaches that explicitly minimize sharpness, Sharpness-Aware Minimization (SAM) has shown potential in enhancing generalization performance on general domain image datasets. This success has led to the development of several advanced sharpness-based algorithms aimed at addressing the limitations of SAM, such as Adaptive SAM, Surrogate-Gap SAM, Weighted SAM, and Curvature Regularized SAM. These sharpness-based optimizers have shown improvements in model generalization compared to conventional stochastic gradient descent optimizers and their variants on general domain image datasets, but they have not been thoroughly evaluated on medical images. This work provides a review of recent sharpness-based methods for improving the generalization of deep learning networks and evaluates the methods’ performance on three medical image datasets, including breast ultrasound, chest X-ray, and colon histopathology images. Our findings indicate that the initial SAM method successfully enhances the generalization of various deep learning models. While Adaptive SAM improves generalization of convolutional neural networks, it fails to do so for vision transformers. Other sharpness-based optimizers, however, do not demonstrate consistent results. The results reveal that contrary to findings in the non-medical domain, SAM is the only recommended sharpness-based optimizer that consistently improves generalization in medical image analysis, and further research is necessary to refine the variants of SAM to enhance generalization performance in this field.https://ieeexplore.ieee.org/document/10994767/Deep learninggeneralizationmedical image analysisloss landscapesharpness-aware minimization |
| spellingShingle | Mohamed Hassan Aleksandar Vakanski Boyu Zhang Min Xian Do Sharpness-Based Optimizers Improve Generalization in Medical Image Analysis? IEEE Access Deep learning generalization medical image analysis loss landscape sharpness-aware minimization |
| title | Do Sharpness-Based Optimizers Improve Generalization in Medical Image Analysis? |
| title_full | Do Sharpness-Based Optimizers Improve Generalization in Medical Image Analysis? |
| title_fullStr | Do Sharpness-Based Optimizers Improve Generalization in Medical Image Analysis? |
| title_full_unstemmed | Do Sharpness-Based Optimizers Improve Generalization in Medical Image Analysis? |
| title_short | Do Sharpness-Based Optimizers Improve Generalization in Medical Image Analysis? |
| title_sort | do sharpness based optimizers improve generalization in medical image analysis |
| topic | Deep learning generalization medical image analysis loss landscape sharpness-aware minimization |
| url | https://ieeexplore.ieee.org/document/10994767/ |
| work_keys_str_mv | AT mohamedhassan dosharpnessbasedoptimizersimprovegeneralizationinmedicalimageanalysis AT aleksandarvakanski dosharpnessbasedoptimizersimprovegeneralizationinmedicalimageanalysis AT boyuzhang dosharpnessbasedoptimizersimprovegeneralizationinmedicalimageanalysis AT minxian dosharpnessbasedoptimizersimprovegeneralizationinmedicalimageanalysis |