Deep learning models have achieved great success in automating skin lesion
diagnosis. However, the ethnic disparity in these models' predictions, where
lesions on darker skin types are usually underrepresented and have lower
diagnosis accuracy, receives little attention. In this paper, we propose
FairDisCo, a disentanglement deep learning framework with contrastive learning
that utilizes an additional network branch to remove sensitive attributes, i.e.
skin-type information from representations for fairness and another contrastive
branch to enhance feature extraction. We compare FairDisCo to three fairness
methods, namely, resampling, reweighting, and attribute-aware, on two newly
released skin lesion datasets with different skin types: Fitzpatrick17k and
Diverse Dermatology Images (DDI). We adapt two fairness-based metrics DPM and
EOM for our multiple classes and sensitive attributes task, highlighting the
skin-type bias in skin lesion classification. Extensive experimental evaluation
demonstrates the effectiveness of FairDisCo, with fairer and superior
performance on skin lesion classification tasks.

本文介绍了一种基于深度学习的公平皮肤病诊断框架 FairDisCo，通过对比学习和删除敏感属性等方法，有效解决了现有模型对较暗皮肤图像的低准确度问题。在两个新的皮肤数据集 Fitzpatrick17k 和 Diverse Dermatology Images 上的评估表明，FairDisCo 在公平性和性能方面的表现均优于其他三种公平性方法。