BrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosis
Multi-site functional MRI (fMRI) studies are essential for robust neuropsychiatric diagnosis yet suffer severe domain shifts from scanner heterogeneity, demographics, and site-specific acquisition protocols. Traditional domain adaptation requires concurrent source and target data access, violating clinical privacy regulations. Moreover, functional connectivity matrices lie on the Symmetric Positive Definite (SPD) manifold, where Euclidean operations cause geometric distortions corrupting diagnostic patterns. We propose BrainRiem, a source-free domain adaptation framework learning compact Riemannian brain prototypes via manifold-aware bi-level optimization. It employs the Log-Euclidean Met...
| Authors | Kunyu Zhang, Tianxiang Xu |
|---|---|
| Primary category | cs.LG |
| Published | 28 June 2026 |
| Last updated at source | 28 June 2026 |
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