XTranPrune: eXplainability-aware Transformer Pruning for Bias Mitigation in Dermatological Disease Classification

Numerous studies have demonstrated the effectiveness of deep learning models in medical image analysis. However, these models often exhibit performance disparities across different demographic cohorts, undermining their trustworthiness in clinical settings. While previous efforts have focused on bias mitigation techniques for traditional encoders, the increasing use of transformers in the medical domain calls for novel fairness enhancement methods. Additionally, the efficacy of explainability methods in improving model fairness remains unexplored. To address these gaps, we introduce XTranPrune, a bias mitigation method tailored for vision transformers. Leveraging state-of-the-art explainability techniques, XTranPrune generates a pruning mask to remove discriminatory modules while preserving performance-critical ones. Our experiments on two skin lesion datasets demonstrate the superior performance of XTranPrune across multiple fairness metrics. The code can be found at https://github.com/AliGhadirii/XTranPrune.