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
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
A Temporal Fusion Transformer with CORAL ordinal layer and autoregressive Mixture Density Network generates multi-horizon probabilistic trajectories and decomposed uncertainty estimates for Alzheimer's progression on ADNI data.
GMN4AD applies graph matching and test-time contrastive adaptation to improve Alzheimer's diagnosis accuracy on heterogeneous multi-center sMRI datasets.
Simple mean-pooling multiple instance learning matches or exceeds complex 3D models for neuroimage classification on most tested datasets and trains far more efficiently.
citing papers explorer
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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.