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.

本研究解决了当前医学图像生成领域中的种族偏见问题，尤其是在皮肤疾病检测中的表现。作者提出了一种名为FairSkin的新型扩散模型，通过三层重抽样机制减轻不同肤色之间的偏差，从而提升生成图像的多样性和质量。这一方法的显著发现是，它在临床环境中实现了更公平的皮肤疾病检测，为减少医疗差异做出了贡献。

公平皮肤：皮肤疾病图像生成的公平扩散