NeuroVFM: Neuroimaging Foundation Model Trained on Uncurated Clinical MRI and CT Data via Vol-JEPA

NeuroVFM is a new foundation model for neuroimaging that uses a self-supervised learning approach called Vol-JEPA (Volumetric Joint Embedding Predictive Architecture) to train on raw, uncurated clinical MRI and CT volumes. Unlike prior approaches that required carefully curated datasets, Vol-JEPA learns rich 3D representations by predicting latent representations of masked volumetric patches, making it far more practical for real-world clinical data pipelines. The model demonstrates strong transfer performance across downstream neuroimaging tasks without task-specific pretraining data. For developers building medical imaging pipelines or working on foundation model adaptation in specialized domains, NeuroVFM is a concrete example of how JEPA-style objectives can replace supervised curation overhead. This is relevant to anyone exploring self-supervised 3D vision models or looking to adapt general foundation model architectures to volumetric, domain-specific data.