Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of Racial Health Inequities

arXiv:2403.18196v1 Announce Type: new
Abstract: There has been significant progress in implementing deep learning models in disease diagnosis using chest X- rays. Despite these advancements, inherent biases in these models can lead to disparities in prediction accuracy across protected groups. In this study, we propose a framework to achieve accurate diagnostic outcomes and ensure fairness across intersectional groups in high-dimensional chest X- ray multi-label classification. Transcending traditional protected attributes, we consider complex interactions within social determinants, enabling a more granular …

abstract analysis arxiv beyond biases classification cs.ai cs.cv cs.cy cs.lg deep learning diagnosis disease disease diagnosis fairness health prediction progress racial ray social type x-ray