BrainRiem learns Riemannian brain prototypes via manifold-aware bi-level optimization and Dirichlet Energy calibration for source-free cross-site fMRI diagnosis.
Ad- vances in neural information processing systems30(2017)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
T-DuMpRa fuses classifier outputs with cosine-matched multi-prototypes from a teacher model via conservative gating, yielding 0.21-2.69% gains on skin lesion datasets across five backbones.
citing papers explorer
-
BrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosis
BrainRiem learns Riemannian brain prototypes via manifold-aware bi-level optimization and Dirichlet Energy calibration for source-free cross-site fMRI diagnosis.
-
T-DuMpRa: Teacher-guided Dual-path Multi-prototype Retrieval Augmented framework for fine-grained medical image classification
T-DuMpRa fuses classifier outputs with cosine-matched multi-prototypes from a teacher model via conservative gating, yielding 0.21-2.69% gains on skin lesion datasets across five backbones.