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
<|reference_start|>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: 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.<|reference_end|>
@article{moukheiber2024looking,
    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},
    author={Dana Moukheiber, Saurabh Mahindre, Lama Moukheiber, Mira Moukheiber,
  Mingchen Gao},
    journal={arXiv preprint arXiv:2403.18196},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18196},
    primaryClass={cs.LG cs.AI cs.CV cs.CY}
}