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Although self-supervised learning enables us to bootstrap the training byexploiting unlabeled data, the generic self-supervised methods for naturalimages do not sufficiently incorporate the context. For medical images, adesirable method should be sensitive enough to detect deviation fromnormal-appearing tissue of each anatomical region; here, anatomy is thecontext. We introduce a novel approach with two levels of self-supervisedrepresentation learning objectives: one on the regional anatomical level andanother on the patient-level. We use graph neural networks to incorporate therelationship between different anatomical regions. The structure of the graphis informed by anatomical correspondences between each patient and ananatomical atlas. In addition, the graph representation has the advantage ofhandling any arbitrarily sized image in full resolution. Experiments onlarge-scale Computer Tomography (CT) datasets of lung images show that ourapproach compares favorably to baseline methods that do not account for thecontext. We use the learned embedding for staging lung tissue abnormalitiesrelated to COVID-19.

Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network