Joint Imaging-ROI Representation Learning via Cross-View Contrastive Alignment for Brain Disorder Classification
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 demonstrated 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 training settings. To address this gap, we propose a unified cross-view contrastive framework for joint imaging-ROI representation learning. Our method learns subject-level...
| Authors | Wei Liang, Lifang He |
|---|---|
| Primary category | cs.CV |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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