MIFR：用于皮肤病分类的模态不变公平表示框架
MIFR: A Modality-Invariant and Fair Representation Framework for Skin Disease Classification
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中文总结 AI 辅助
本研究提出MIFR框架，通过配对临床与皮肤镜图像的多目标损失训练，实现皮肤病分类的模态不变性与公平性，在多数据集上验证了其预测性能与公平性。
中文摘要 AI 辅助
皮肤病是全球重大公共卫生负担，但辅助诊断的机器学习工具存在两个关键局限：仅依赖单一模态诊断，且不同肤色群体的系统性能存在差异。现有方法分别应对这两个挑战，本研究提出用于皮肤病分类的模态不变公平表示框架（MIFR）。该架构采用基于ViT的编码器将临床照片与皮肤镜图像配对，通过模态特定投影头将每个输入投影到高维嵌入空间。所得模型采用五组件多目标损失训练，包括用于分类的加权交叉熵、用于公平性的混淆和皮肤类型分类损失、用于类别对齐的单模态监督对比损失，以及用于临床与皮肤镜模态对齐的模态不变损失。在HIBA+Derm7pt配对数据集及外部PAD-UFES-20和ISIC 2019数据集上的实验表明，模态不变表示学习可提供与相关基线模型相当的预测性能，且在内部数据集上具备相当的公平性。t-SNE可视化证实，同一疾病的临床与皮肤镜嵌入在几何上对齐，验证了联合目标的有效性。
英文摘要
Skin diseases represent a major global public health burden, yet machine learning tools developed to assist in their diagnosis suffer from two critical limitations: reliance on only one modality for diagnosis and systematic performance disparities across skin tones. While existing approaches address each challenge separately, this work proposes a modality-invariant framework with fair representation (MIFR) for skin disease classification. The architecture pairs clinical photographs with dermoscopic images using ViT-based encoders, projecting each input into a high-dimensional embedding space via modality-specific projection heads. The resulting model is trained with a five-component multi-objective loss including weighted cross-entropy for classification, confusion and skin-type classification losses for fairness, per-modality supervised contrastive loss for class alignment, and a modality-invariance loss for clinical and dermoscopic modality alignment. Experiments on the HIBA+Derm7pt paired dataset and the external PAD-UFES-20 and ISIC 2019 datasets showed that modality-invariant representation learning provides competitive predictive performance compare to relevant baseline models and competitive fairness on the internal dataset. t-SNE visualizations confirmed that clinical and dermoscopic embeddings of the same disease are geometrically aligned, validating the joint objectives.
发表机构
- University of Dschang（德昌大学）
- Université Paris-Panthéon-Assas（巴黎先贤祠-阿萨斯大学）
- Université de Picardie Jules Verne（儒勒·凡尔纳皮卡第大学）
- Hertie Institute for AI in Brain Health, University of Tübingen（蒂宾根大学赫蒂脑健康人工智能研究所）
机构由 AI 辅助整理，请以论文原文为准。
