Meet NeuroVFM: A New Neuroimaging Foundation Model Trained With Vol-JEPA on Uncurated Clinical MRI and CT Volumes

Foundation Model Trained With Vol-JEPA on Uncurated Clinical MRI and CT Volumes">

Frontier models learn mostly from public internet data. However, clinical neuroimaging rarely appears there, because MRI and CT scans contain identifiable facial features. Consequently, general models underperform on brain-imaging tasks. A University of Michigan research team addresses this gap with NeuroVFM , published in Nature Medicine .

At its core, NeuroVFM is a generalist visual foundation model for neuroimaging. Specifically, it was trained on 5.24 million clinical MRI and CT volumes. These came from 566,915 studies in the UM-NeuroImages dataset. That data spans over two decades of routine care at Michigan Medicine.

The research team call their approach ‘health system learning.’ In short, the model learns from uncurated data generated during normal clinical operations. Therefore, it avoids the bottleneck of paired radiology reports. It also avoids the disease-specific curation used in narrow classifiers.

Notably, the base model is called Vol-JEPA. It extends the earlier I-JEPA and V-JEPA methods to volumetric medical images. This reflects a wider trend: JEPA-style learning is expanding into medical imaging.

Vol-JEPA is a self-supervised, vision-only algorithm. Rather than reconstructing pixels, it predicts representations in a learned latent space. As a result, it needs no labels, no report text, and no voxel decoder.

First, each 3D volume is tokenized into non-overlapping 4×16×16-voxel patches. Next, the volume is split into a small visible context and a larger masked target. A student encoder then processes the context patches.

Meanwhile, a predictor combines context latents with target position encodings. It predicts the masked-region latents. A teacher encoder generates the ground-truth target latents. This teacher is an exponential moving average (EMA) of the student. Training minimizes a smooth L1 loss between predicted and teacher latents, with gradients stopped through the teacher.

Importantly, masking is foreground-focused, using precomputed head masks. Context ratios are 25% for MRI and 20% for CT, with 20% patch dropout. This design encourages the encoder to model shared neuroanatomy rather than background shortcuts.

To measure this, the research team froze every encoder and trained identical study-level attentive probes. The primary endpoint was macro-averaged AUROC across 156 diagnostic tasks. These cover 74 MRI and 82 CT diagnoses.

Consequently, NeuroVFM reached 92.68 AUROC on CT and 92.49 on MRI. Moreover, it outperformed every baseline on the aggregate endpoint.