bibtext: @InProceedings{ Mou_Evaluating_MICCAI2024,
                   author = { Mouheb, Kaouther and Elbatel, Marawan and Klein, Stefan and Bron, Esther E. },
                   title = { { Evaluating the Fairness of Neural Collapse in Medical Image 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: Deep learning has achieved impressive performance across various medical imaging tasks. However, its inherent bias against specific groups hinders its clinical applicability in equitable healthcare systems. A recently discovered phenomenon, Neural Collapse (NC), has shown potential in improving the generalization of state-of-the-art deep learning models. Nonetheless, its implications on bias in medical imaging remain unexplored. Our study investigates deep learning fairness through the lens of NC. We analyze the training dynamics of models as they approach NC when training using biased datasets, and examine the subsequent impact on test performance, specifically focusing on label bias. We find that biased training initially results in different NC configurations across subgroups, before converging to a final NC solution by memorizing all data samples. Through extensive experiments on three medical imaging datasets—PAPILA, HAM10000, and CheXpert—we find that in biased settings, NC can lead to a significant drop in F1 score across all subgroups. Our code is available at https://gitlab.com/radiology/neuro/neural-collapse-fairness.
title: Evaluating the Fairness of Neural Collapse in Medical Image Classification
authors: Mouheb, Kaouther Elbatel, Marawan Klein, Stefan Bron, Esther E.