MAE with spectral-domain reconstruction loss outperforms other self-supervised methods for MRI disease detection when the signal involves high-frequency anatomical structures.
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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.
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.
citing papers explorer
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Masked and Predictive Self-Supervised Foundation Models for 3D Brain MRI
MAE with spectral-domain reconstruction loss outperforms other self-supervised methods for MRI disease detection when the signal involves high-frequency anatomical structures.
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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.