Joint Imaging-ROI Representation Learning via Cross-View Contrastive Alignment for Brain Disorder Classification

ArXi:2603.10253v1 Announce Type: cross Brain imaging classification is commonly approached from two perspectives: modeling the full image volume to capture global anatomical context, or constructing ROI-based graphs to encode localized and topological interactions. Although both representations have nstrated independent efficacy, their relative contributions and potential complementarity remain insufficiently understood. Existing fusion approaches are typically task-specific and do not enable controlled evaluation of each representation under consistent.