Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective
Fuente:
arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866915308099338240 |
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| author | Oh, Yujin Jin, Pengfei Park, Sangjoon Kim, Sekeun Yoon, Siyeop Kim, Kyungsang Kim, Jin Sung Li, Xiang Li, Quanzheng |
| author_facet | Oh, Yujin Jin, Pengfei Park, Sangjoon Kim, Sekeun Yoon, Siyeop Kim, Kyungsang Kim, Jin Sung Li, Xiang Li, Quanzheng |
| contents | Ensuring fairness in medical image segmentation is critical due to biases in imbalanced clinical data acquisition caused by demographic attributes (e.g., age, sex, race) and clinical factors (e.g., disease severity). To address these challenges, we introduce Distribution-aware Mixture of Experts (dMoE), inspired by optimal control theory. We provide a comprehensive analysis of its underlying mechanisms and clarify dMoE's role in adapting to heterogeneous distributions in medical image segmentation. Furthermore, we integrate dMoE into multiple network architectures, demonstrating its broad applicability across diverse medical image analysis tasks. By incorporating demographic and clinical factors, dMoE achieves state-of-the-art performance on two 2D benchmark datasets and a 3D in-house dataset. Our results highlight the effectiveness of dMoE in mitigating biases from imbalanced distributions, offering a promising approach to bridging control theory and medical image segmentation within fairness learning paradigms. The source code will be made available. The source code is available at https://github.com/tvseg/dMoE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00619 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective Oh, Yujin Jin, Pengfei Park, Sangjoon Kim, Sekeun Yoon, Siyeop Kim, Kyungsang Kim, Jin Sung Li, Xiang Li, Quanzheng Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Ensuring fairness in medical image segmentation is critical due to biases in imbalanced clinical data acquisition caused by demographic attributes (e.g., age, sex, race) and clinical factors (e.g., disease severity). To address these challenges, we introduce Distribution-aware Mixture of Experts (dMoE), inspired by optimal control theory. We provide a comprehensive analysis of its underlying mechanisms and clarify dMoE's role in adapting to heterogeneous distributions in medical image segmentation. Furthermore, we integrate dMoE into multiple network architectures, demonstrating its broad applicability across diverse medical image analysis tasks. By incorporating demographic and clinical factors, dMoE achieves state-of-the-art performance on two 2D benchmark datasets and a 3D in-house dataset. Our results highlight the effectiveness of dMoE in mitigating biases from imbalanced distributions, offering a promising approach to bridging control theory and medical image segmentation within fairness learning paradigms. The source code will be made available. The source code is available at https://github.com/tvseg/dMoE. |
| title | Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.00619 |