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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A 3D self-supervised foundation model trained on over 360k head CT scans improves downstream disease classification on limited-label internal and external datasets versus scratch-trained and prior models.
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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.