bibtext: @InProceedings{ Gha_XTranPrune_MICCAI2024,
                   author = { Ghadiri, Ali and Pagnucco, Maurice and Song, Yang },
                   title = { { XTranPrune: eXplainability-aware Transformer Pruning for Bias Mitigation in Dermatological Disease Classification } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15010 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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.
title: XTranPrune: eXplainability-aware Transformer Pruning for Bias Mitigation in Dermatological Disease Classification
authors: Ghadiri, Ali Pagnucco, Maurice Song, Yang