  Skin lesions can be an early indicator of a wide range of infectious and
other diseases. The use of deep learning (DL) models to diagnose skin lesions
has great potential in assisting clinicians with prescreening patients.
However, these models often learn biases inherent in training data, which can
lead to a performance gap in the diagnosis of people with light and/or dark
skin tones. To the best of our knowledge, limited work has been done on
identifying, let alone reducing, model bias in skin disease classification and
segmentation. In this paper, we examine DL fairness and demonstrate the
existence of bias in classification and segmentation models for subpopulations
with darker skin tones compared to individuals with lighter skin tones, for
specific diseases including Lyme, Tinea Corporis and Herpes Zoster. Then, we
propose a novel preprocessing, data alteration method, called EdgeMixup, to
improve model fairness with a linear combination of an input skin lesion image
and a corresponding a predicted edge detection mask combined with color
saturation alteration. For the task of skin disease classification, EdgeMixup
outperforms much more complex competing methods such as adversarial approaches,
achieving a 10.99% reduction in accuracy gap between light and dark skin tone
samples, and resulting in 8.4% improved performance for an underrepresented
subpopulation.
