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

• URL: http://arxiv.org/abs/2502.00619v1
• Date: Sun, 02 Feb 2025 01:10:31 GMT
• Title: Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective
• Authors: Yujin Oh, Pengfei Jin, Sangjoon Park, Sekeun Kim, Siyeop Yoon, Kyungsang Kim, Jin Sung Kim, Xiang Li, Quanzheng Li,
• Abstract summary: Distribution-aware Mixture of Experts (dMoE) is inspired by optimal control theory. We provide an analysis of its underlying mechanisms and clarify dMoE's role in adapting to heterogeneous distributions in medical image segmentation. dMoE achieves state-of-the-art performance on two 2D benchmark datasets and a 3D in-house dataset.
• Score: 16.585699834768395
• License:
• Abstract: 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.

Related papers

• KAN-Mamba FusionNet: Redefining Medical Image Segmentation with Non-Linear Modeling [3.2971993272923443]
  This research presents an innovative methodology that combines Kolmogorov-Arnold Networks (KAN) with an adapted Mamba layer for medical image segmentation. The proposed KAN-Mamba FusionNet framework improves image segmentation by integrating attention-driven mechanisms with convolutional parallel training and autoregressive deployment.
  arXiv  Detail & Related papers  (2024-11-18T09:19:16Z)
• FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical Imaging [68.6715007665896]
  FedMedICL is a unified framework and benchmark to holistically evaluate federated medical imaging challenges. We comprehensively evaluate several popular methods on six diverse medical imaging datasets. We find that a simple batch balancing technique surpasses advanced methods in average performance across FedMedICL experiments.
  arXiv  Detail & Related papers  (2024-07-11T19:12:23Z)
• An Empirical Study on the Fairness of Foundation Models for Multi-Organ Image Segmentation [15.377701636336784]
  We prospectively curate a benchmark dataset of 3D MRI and CT scans of the organs including liver, kidney, spleen, lung and aorta. We document demographic details such as gender, age, and body mass index (BMI) for each subject to facilitate a nuanced fairness analysis. Our comprehensive analysis, which accounts for various confounding factors, reveals significant fairness concerns within these foundational models.
  arXiv  Detail & Related papers  (2024-06-18T14:14:04Z)
• C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network [67.97926983664676]
  We propose a cross-modal consistent multi-view medical report generation with a domain transfer network (C2M-DoT) C2M-DoT substantially outperforms state-of-the-art baselines in all metrics.
  arXiv  Detail & Related papers  (2023-10-09T02:31:36Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels [54.58539616385138]
  We introduce a novel semi-supervised 2D medical image segmentation framework termed Mine yOur owN Anatomy (MONA) First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features.
  arXiv  Detail & Related papers  (2022-09-27T15:50:31Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation [35.33425093398756]
  Unlabeled data is much easier to acquire than well-annotated data. We propose uncertainty-aware multi-view co-training for medical image segmentation. Our framework is capable of efficiently utilizing unlabeled data for better performance.
  arXiv  Detail & Related papers  (2020-06-28T22:04:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.