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.

翻译：基于胸部X光片实现疾病诊断的深度学习模型已取得显著进展。尽管有这些进展，这些模型固有的偏差可能导致在不同受保护群体间的预测准确性差异。在本研究中，我们提出一个框架，旨在实现高维胸部X光多标签分类中跨交叉群体的准确诊断结果并确保公平性。超越传统的受保护属性，我们考虑社会决定因素中的复杂交互作用，从而能够进行更细粒度的基准测试和公平性评估。我们提出一种简单而稳健的方法，涉及使用跨群体平衡数据集对预训练模型的最后一层分类层进行重训练。此外，我们考虑公平性约束，并针对多标签设置集成类别平衡微调。在MIMIC-CXR数据集上的评估显示，与基线方法相比，我们的框架在准确性与公平性之间实现了最优权衡。