  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 3.4% and 14.4% in mean AUC from both a
previous contrastive method and ImageNet pretrained baseline respectively. 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.
