Self-supervised contrastive learning between pairs of multiple views of the
same image has been shown to successfully leverage unlabeled data to produce
meaningful visual representations for both natural and medical images. However,
there has been limited work on determining how to select pairs for medical
images, where availability of patient metadata can be leveraged to improve
representations. In this work, we develop a method to select positive pairs
coming from views of possibly different images through the use of patient
metadata. We compare strategies for selecting positive pairs for chest X-ray
interpretation including requiring them to be from the same patient, imaging
study or laterality. We evaluate downstream task performance by fine-tuning the
linear layer on 1% of the labeled dataset for pleural effusion classification.
Our best performing positive pair selection strategy, which involves using
images from the same patient from the same study across all lateralities,
achieves a performance increase of 14.4% in mean AUC from the ImageNet
pretrained baseline. Our controlled experiments show that the keys to improving
downstream performance on disease classification are (1) using patient metadata
to appropriately create positive pairs from different images with the same
underlying pathologies, and (2) maximizing the number of different images used
in query pairing. In addition, we explore leveraging patient metadata to select
hard negative pairs for contrastive learning, but do not find improvement over
baselines that do not use metadata. Our method is broadly applicable to medical
image interpretation and allows flexibility for incorporating medical insights
in choosing pairs for contrastive learning.

该研究提出了一种利用患者元数据为医学影像选择正向对样本的方法，使用该方法选择出来的正向对样本可以在自我监督对比学习中提高下游任务的性能。最佳的正向对选择策略将来自同一病人，同一研究的图像从所有的位置进行捕获，使平均 AUC 增长了 14.4％。