Context Matters: Graph-based Self-supervised Representation Learning for Medical Images.

ABSTRACT: 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 t

SUBMITTER: Sun L 

PROVIDER: S-EPMC8513790 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature