  Supervised learning method requires a large volume of annotated datasets.
Collecting such datasets is time-consuming and expensive. Until now, very few
annotated COVID-19 imaging datasets are available. Although self-supervised
learning enables us to bootstrap the training by exploiting unlabeled data, the
generic self-supervised methods for natural images do not sufficiently
incorporate the context. For medical images, a desirable method should be
sensitive enough to detect deviation from normal-appearing tissue of each
anatomical region; here, anatomy is the context. We introduce a novel approach
with two levels of self-supervised representation learning objectives: one on
the regional anatomical level and another on the patient-level. We use graph
neural networks to incorporate the relationship between different anatomical
regions. The structure of the graph is informed by anatomical correspondences
between each patient and an anatomical atlas. In addition, the graph
representation has the advantage of handling any arbitrarily sized image in
full resolution. Experiments on large-scale Computer Tomography (CT) datasets
of lung images show that our approach compares favorably to baseline methods
that do not account for the context. We use the learnt embedding to quantify
the clinical progression of COVID-19 and show that our method generalizes well
to COVID-19 patients from different hospitals. Qualitative results suggest that
our model can identify clinically relevant regions in the images.
