Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis.

ABSTRACT: Recent self-supervised advances in medical computer vision exploit the global and local anatomical self-similarity for pretraining prior to downstream tasks such as segmentation. However, current methods assume i.i.d. image acquisition, which is invalid in clinical study designs where follow-up longitudinal scans track subject-specific temporal changes. Further, existing self-supervised methods for medically-relevant image-to-image architectures exploit only spatial or temporal self-similarity and do so via a loss applied only at a single image-scale, with naive multi-scale spatiotemporal extensions collapsing to degenerate solutions. To these ends, this paper makes two contributions: (1) It presents a local and multi-scale spatiotemporal representation learning method for image-to-image a

SUBMITTER: Ren M 

PROVIDER: S-EPMC10445502 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature