BID-Net: Balanced Incremental Distillation Network for Fair Dermatological Disease Diagnosis

Yiqin Luo, Tianlong Gu, Fengrui Hao, Liang Chang

Abstract: Given the increasing prevalence of deep learning applications in dermatological disease diagnosis, the pursuit of diagnostic accuracy needs to be accompanied by a focus on decision-making fairness to avoid unfair discrimination against under-represented demographic groups. This requires a tradeoff between diagnostic accuracy and fairness. To this end, we propose a balanced incremental distillation network (BID-Net) to tackle this problem, which balances the learning of different groups by being sensitive to changes in the data distribution. Specifically, aided by balanced memory, representative demographic groups are designed to assist underrepresented groups in learning knowledge, which is incrementally trained by integrating the distributions of different groups. In addition, our BID-Net incorporates knowledge distillation and distributional disparity to alleviate the catastrophic forgetting and enhance fairness. Experiments on two skin datasets demonstrate that our proposed network outperforms other methods in terms of fairness criteria and the trade-off between fairness and performance.

External IDs:dblp:conf/icassp/LuoGH025