A foundation model for generalized brain MRI analysis.

ABSTRACT: Artificial intelligence (AI) applied to brain magnetic resonance imaging (MRI) has the potential to improve disease diagnosis and management but requires algorithms with generalizable knowledge that can perform well in a variety of clinical scenarios. The field has been constrained, thus far, by limited training data and task-specific models that do not generalize well across patient populations and medical tasks. Foundation models, by leveraging self-supervised learning, pretraining, and targeted adaptation, present a promising paradigm to overcome these limitations. Here, we present Brain Imaging Adaptive Core (BrainIAC), a novel foundation model designed to learn generalized representations from unlabeled brain MRI data and serve as a core basis for diverse downstream application adapta

SUBMITTER: Tak D 

PROVIDER: S-EPMC11643205 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature