  This paper presents a novel positive and negative set selection strategy for
contrastive learning of medical images based on labels that can be extracted
from clinical data. In the medical field, there exists a variety of labels for
data that serve different purposes at different stages of a diagnostic and
treatment process. Clinical labels and biomarker labels are two examples. In
general, clinical labels are easier to obtain in larger quantities because they
are regularly collected during routine clinical care, while biomarker labels
require expert analysis and interpretation to obtain. Within the field of
ophthalmology, previous work has shown that clinical values exhibit
correlations with biomarker structures that manifest within optical coherence
tomography (OCT) scans. We exploit this relationship by using the clinical data
as pseudo-labels for our data without biomarker labels in order to choose
positive and negative instances for training a backbone network with a
supervised contrastive loss. In this way, a backbone network learns a
representation space that aligns with the clinical data distribution available.
Afterwards, we fine-tune the network trained in this manner with the smaller
amount of biomarker labeled data with a cross-entropy loss in order to classify
these key indicators of disease directly from OCT scans. We also expand on this
concept by proposing a method that uses a linear combination of clinical
contrastive losses. We benchmark our methods against state of the art
self-supervised methods in a novel setting with biomarkers of varying
granularity. We show performance improvements by as much as 5\% in total
biomarker detection AUROC.
