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Found 4 papers, 4 papers with code
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes
Coarse labels do not capture the long-tailed spectrum of brain disorder phenotypes, which leads to a loss of generalizability of the model that makes them less useful in diagnostic settings.
Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data
We show that self-supervised models are not as robust as expected based on their results in natural imaging benchmarks and can be outperformed by supervised learning with dropout.
Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's Disease
Sensory input from multiple sources is crucial for robust and coherent human perception.
On self-supervised multi-modal representation learning: An application to Alzheimer's disease
In this paper, we introduce a way to exhaustively consider multimodal architectures for contrastive self-supervised fusion of fMRI and MRI of AD patients and controls.
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