TAF-Net adaptively fuses longitudinal structural MRI via a temporal gate to achieve top performance in 3-year MCI-to-AD conversion prediction on ADNI using only MRI.
Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation
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Neuro-JEPA is a sparse multimodal foundation model pretrained on 1,551,862 brain MRI scans that shows stronger and more consistent performance than existing models and CNN baselines across 47 tasks from clinical and public datasets.
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Adaptive Temporal Gating of Longitudinal Magnetic Resonance Imaging for Alzheimer's Prediction
TAF-Net adaptively fuses longitudinal structural MRI via a temporal gate to achieve top performance in 3-year MCI-to-AD conversion prediction on ADNI using only MRI.
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Learning Sparse Latent Predictive Foundation Model for Multimodal Neuroimaging
Neuro-JEPA is a sparse multimodal foundation model pretrained on 1,551,862 brain MRI scans that shows stronger and more consistent performance than existing models and CNN baselines across 47 tasks from clinical and public datasets.