Finite-sample noise collapses the eigengap in representation covariances limiting recoverable modes K(N); multimodal learning stabilizes it via low-rank constraints, yielding better class separation quantified by truncated Mahalanobis energy approximated with a zeta function.
OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease
2 Pith papers cite this work. Polarity classification is still indexing.
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Simple mean-pooling multiple instance learning matches or exceeds complex 3D models for neuroimage classification on most tested datasets and trains far more efficiently.
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Anchoring the Eigengap: Cross-Modal Spectral Stabilization for Sample-Efficient Representation Learning
Finite-sample noise collapses the eigengap in representation covariances limiting recoverable modes K(N); multimodal learning stabilizes it via low-rank constraints, yielding better class separation quantified by truncated Mahalanobis energy approximated with a zeta function.
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A Multi-Dataset Benchmark of Multiple Instance Learning for 3D Neuroimage Classification
Simple mean-pooling multiple instance learning matches or exceeds complex 3D models for neuroimage classification on most tested datasets and trains far more efficiently.