Algorithmic Fairness in Lesion Classification by Mitigating Class Imbalance and Skin Tone Bias

Deep learning models have shown considerable promise in the classification of skin lesions. However, a notable challenge arises from their inherent bias towards dominant skin tones and the issue of imbalanced class representation. This study introduces a novel data augmentation technique designed to address these limitations. Our approach harnesses contextual information from the prevalent class to synthesize various samples representing minority classes. Using a mixup-based algorithm guided by an adaptive sampler, our method effectively tackles bias and class imbalance issues. The adaptive sampler dynamically adjusts sampling probabilities based on the network's meta-set performance, enhancing overall accuracy. Our research demonstrates the efficacy of this approach in mitigating skin tone bias and achieving robust lesion classification across a spectrum of diverse skin colors from two distinct benchmark datasets, offering promising implications for improving dermatological diagnostic systems.