mlsanityData to 7 Oct 2026

Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective

arXiv 2502.00619 ↗First posted 2 Feb 2025eess.IV cs.AI cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 37 references that are in the map. First posted on arXiv 2 Feb 2025.

Builds on

21 of 37 references.

References · 21

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9 more
  1. 2024FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Modelscited by 47
  2. 2024Divide and not forget: Ensemble of selectively trained experts in Continual Learningcited by 38
  3. 2024M4oE: A Foundation Model for Medical Multimodal Image Segmentation with Mixture of Expertscited by 26
  4. 2023LLM-driven Multimodal Target Volume Contouring in Radiation Oncologycited by 20
  5. 2024FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classificationcited by 19
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026DuetMoE: Coupling Inter- and Intra-Subgroup Robustness for Fair Medical Image Analysiscited by 0

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