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
Dana Moukheiber Saurabh Mahindre Lama Moukheiber Mira Moukheiber Mingchen Gao
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.