解决多模态医疗分类中数据偏倚与群体不公平问题
MultiFair: Multimodal Balanced Fairness-Aware Medical Classification with Dual-Level Gradient Modulation
- 通过双层梯度调节，动态平衡不同模态的学习进度
- 在三个真实医疗数据集上提升公平性，尤其在缺失模态下表现稳定
- 适合关注医疗AI公平性与鲁棒性的研究者和开发者
医疗决策系统越来越依赖多源数据以实现可靠且无偏的诊断。然而，现有多模态学习模型未能达成此目标，主要因忽略两大挑战：其一，不同数据模态可能学习不均衡，导致模型偏向某些模态；其二，模型可能过度关注特定人口群体，造成不公平性能。这两者相互影响——不同模态在优化过程中可能偏好特定群体，从而引发不平衡与不公平的多模态学习。本文提出一种新方法 MultiFair，采用双层梯度调制机制，在数据模态与人群群体两个层面动态调节训练梯度的方向与大小。我们在包含多类别分类与缺失模态设置的三个真实世界医疗分类数据集上评估 MultiFair，实验结果证明其有效性。
原文摘要 · Abstract (English)
Medical decision systems increasingly rely on data from multiple sources to ensure reliable and unbiased diagnosis. However, existing multimodal learning models fail to achieve this goal because they often overlook two critical challenges. First, various data modalities may learn unevenly, thereby converging to a model biased towards certain modalities. Second, the model may emphasize learning on certain demographic groups causing unfair performances. The two aspects can influence each other, as different data modalities may favor respective groups during optimization, leading to both imbalanced and unfair multimodal learning. This paper proposes a novel approach called MultiFair for multimodal medical classification, which addresses these challenges with a dual-level gradient modulation process. MultiFair dynamically modulates training gradients regarding the optimization direction and magnitude at both data modality and group levels. We evaluate MultiFair on three real-world medical classification datasets with diverse demographic attributes,including multiclass classification and missing-modality settings. Experimental results demonstrate its effectiveness.
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