  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.
