BrainSCL: Subtype-Guided Contrastive Learning for Brain Disorder Diagnosis

ArXi:2603.19295v1 Announce Type: new Mental disorder populations exhibit pronounced heterogeneity -- that is, the significant differences between samples -- poses a significant challenge to the definition of positive pairs in contrastive learning. To address this, we propose a subtype-guided contrastive learning framework that models patient heterogeneity as latent subtypes and incorporates them as structural priors to guide discriminative representation learning.