Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network
Abstract
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 learned embedding for staging lung tissue abnormalities related to COVID-19.
Cite
@article{arxiv.2207.02957,
title = {Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network},
author = {Li Sun and Ke Yu and Kayhan Batmanghelich},
journal= {arXiv preprint arXiv:2207.02957},
year = {2022}
}
Comments
Accepted by NeurIPS workshop 2020. arXiv admin note: substantial text overlap with arXiv:2012.06457