A structure-aware VAE generates realistic FC matrices for replay, combined with multi-level knowledge distillation and hierarchical contextual bandit sampling, to enable continual fMRI-based brain disorder diagnosis across sequentially arriving multi-site data without catastrophic forgetting.
Automated anatomical labeling of activations in spm using a macroscopic anatomical parcellation of the mni mri single-subject brain.Neuroimage, 15(1):273–289
2 Pith papers cite this work. Polarity classification is still indexing.
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CORE decouples site confounders in fMRI networks, profiles transient dynamics on a population scaffold using line graphs, and applies subject-adaptive gating to achieve up to 6.7% better cross-site generalization on ABIDE, REST-meta-MDD, SRPBS, and ABCD datasets.
citing papers explorer
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Continual Learning for fMRI-Based Brain Disorder Diagnosis via Functional Connectivity Matrices Generative Replay
A structure-aware VAE generates realistic FC matrices for replay, combined with multi-level knowledge distillation and hierarchical contextual bandit sampling, to enable continual fMRI-based brain disorder diagnosis across sequentially arriving multi-site data without catastrophic forgetting.
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When Brain Networks Travel: Learning Beyond Site
CORE decouples site confounders in fMRI networks, profiles transient dynamics on a population scaffold using line graphs, and applies subject-adaptive gating to achieve up to 6.7% better cross-site generalization on ABIDE, REST-meta-MDD, SRPBS, and ABCD datasets.