bibtext: @InProceedings{ Ans_Algorithmic_MICCAI2024,
                   author = { Ansari, Faizanuddin and Chakraborti, Tapabrata and Das, Swagatam },
                   title = { { Algorithmic Fairness in Lesion Classification by Mitigating Class Imbalance and Skin Tone Bias } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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.
title: Algorithmic Fairness in Lesion Classification by Mitigating Class Imbalance and Skin Tone Bias
authors: Ansari, Faizanuddin Chakraborti, Tapabrata Das, Swagatam