Improving Fairness of Automated Chest X-ray Diagnosis by Contrastive
  Learning
Mingquan Lin Tianhao Li Zhaoyi Sun Gregory Holste Ying Ding Fei Wang George Shih Yifan Peng
Purpose: Limited studies exploring concrete methods or approaches to tackle
and enhance model fairness in the radiology domain. Our proposed AI model
utilizes supervised contrastive learning to minimize bias in CXR diagnosis.
  Materials and Methods: In this retrospective study, we evaluated our proposed
method on two datasets: the Medical Imaging and Data Resource Center (MIDRC)
dataset with 77,887 CXR images from 27,796 patients collected as of April 20,
2023 for COVID-19 diagnosis, and the NIH Chest X-ray (NIH-CXR) dataset with
112,120 CXR images from 30,805 patients collected between 1992 and 2015. In the
NIH-CXR dataset, thoracic abnormalities include atelectasis, cardiomegaly,
effusion, infiltration, mass, nodule, pneumonia, pneumothorax, consolidation,
edema, emphysema, fibrosis, pleural thickening, or hernia. Our proposed method
utilizes supervised contrastive learning with carefully selected positive and
negative samples to generate fair image embeddings, which are fine-tuned for
subsequent tasks to reduce bias in chest X-ray (CXR) diagnosis. We evaluated
the methods using the marginal AUC difference ($\delta$ mAUC).
  Results: The proposed model showed a significant decrease in bias across all
subgroups when compared to the baseline models, as evidenced by a paired T-test
(p<0.0001). The $\delta$ mAUC obtained by our method were 0.0116 (95\% CI,
0.0110-0.0123), 0.2102 (95% CI, 0.2087-0.2118), and 0.1000 (95\% CI,
0.0988-0.1011) for sex, race, and age on MIDRC, and 0.0090 (95\% CI,
0.0082-0.0097) for sex and 0.0512 (95% CI, 0.0512-0.0532) for age on NIH-CXR,
respectively.
  Conclusion: Employing supervised contrastive learning can mitigate bias in
CXR diagnosis, addressing concerns of fairness and reliability in deep
learning-based diagnostic methods.