Self-supervised learning of neighborhood embedding for longitudinal MRI.

ABSTRACT: In recent years, several deep learning models recommend first to represent Magnetic Resonance Imaging (MRI) as latent features before performing a downstream task of interest (such as classification or regression). The performance of the downstream task generally improves when these latent representations are explicitly associated with factors of interest. For example, we derived such a representation for capturing brain aging by applying self-supervised learning to longitudinal MRIs and then used the resulting encoding to automatically identify diseases accelerating the aging of the brain. We now propose a refinement of this representation by replacing the linear modeling of brain aging with one that is consistent in local neighborhoods in the latent space. Called Longitudinal Neighborhoo

SUBMITTER: Ouyang J 

PROVIDER: S-EPMC10168684 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature