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.
Abstract (translated)
在使用胸部X光片进行疾病诊断时,使用深度学习模型已经取得了显著进展。然而,这些模型的固有偏见可能导致不同保护群体之间的预测准确性差异。在这项研究中,我们提出了一个框架,以实现准确诊断结果和确保高维胸部X光多标签分类中交集群体之间的公平性。超越传统的保护属性,我们考虑了社会决定因素内复杂的相互作用,使得公平基准和评估更加精确。我们提出了一个简单而鲁棒的方法,涉及使用平衡数据集重新训练预训练模型的最末层分类层。此外,我们还考虑了公平约束,并针对多标签设置进行了类别平衡微调。在MIMIC-CXR数据集上评估我们的方法,证明了我们的框架在准确性和公平性之间实现了最优的平衡。
URL
https://arxiv.org/abs/2403.18196
