2026
ADAPTING HFMCA TO GRAPH DATA: SELF-SUPERVISED LEARNING FOR GENERALIZABLE FMRI REPRESENTATIONS
ICASSP 2026poster
Functional magnetic resonance imaging (fMRI) analysis faces significant challenges due to limited dataset sizes and domain variability between studies. Traditional self-supervised learning methods inspired by computer vision often rely on positive and negative sample pairs, which can be problematic…