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

• URL: http://arxiv.org/abs/2403.18196v1
• Date: Wed, 27 Mar 2024 02:13:20 GMT
• Title: 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
• Authors: Dana Moukheiber, Saurabh Mahindre, Lama Moukheiber, Mira Moukheiber, Mingchen Gao,
• Abstract summary: Inherited biases in deep learning models can lead to disparities in prediction accuracy across protected groups. We propose a framework to achieve accurate diagnostic outcomes and ensure fairness across intersectional groups.
• Score: 4.351859373879489
• License: http://creativecommons.org/licenses/by/4.0/
• 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 benchmark and evaluation of fairness. We present a simple and robust method that involves retraining the last classification layer of pre-trained models using a balanced dataset across groups. Additionally, we account for fairness constraints and integrate class-balanced fine-tuning for multi-label settings. The evaluation of our method on the MIMIC-CXR dataset demonstrates that our framework achieves an optimal tradeoff between accuracy and fairness compared to baseline methods.

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