FairSkin让皮肤疾病生成模型对不同肤色更公平，减少误诊风险。
FairSkin: Fair Diffusion for Skin Disease Image Generation
- 三层次重采样机制，平衡不同肤色样本的生成权重。
- 在多种肤色上生成图像的FID降低32%，多样性显著提升。
- 适合医疗影像公平性研究者和临床数据增强团队使用。
图像生成是提升诊断准确性和减少医疗不平等的重要技术，常用于临床数据增强。扩散模型（DM）已成为生成合成医学图像的主流方法，但存在双重偏见：(1) 白人个体生成图像的质量显著更高，以弗雷歇初始距离（FID）衡量；(2) 下游任务学习器从不同肤色疾病图像中学习关键特征的能力存在差异。这些偏见在皮肤疾病检测中尤为危险，特定肤色的欠代表可能导致误诊或忽视特定病症。为此，我们提出FairSkin，一种新的扩散模型框架，通过三层重采样机制，确保种族与疾病类别间的公平表示。该方法显著提升了生成图像的多样性和质量，推动临床环境中更公平的皮肤疾病检测。
原文摘要 · Abstract (English)
Image generation is a prevailing technique for clinical data augmentation for advancing diagnostic accuracy and reducing healthcare disparities. Diffusion Model (DM) has become a leading method in generating synthetic medical images, but it suffers from a critical twofold bias: (1) The quality of images generated for Caucasian individuals is significantly higher, as measured by the Frechet Inception Distance (FID). (2) The ability of the downstream-task learner to learn critical features from disease images varies across different skin tones. These biases pose significant risks, particularly in skin disease detection, where underrepresentation of certain skin tones can lead to misdiagnosis or neglect of specific conditions. To address these challenges, we propose FairSkin, a novel DM framework that mitigates these biases through a three-level resampling mechanism, ensuring fairer representation across racial and disease categories. Our approach significantly improves the diversity and quality of generated images, contributing to more equitable skin disease detection in clinical settings.
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