MRI-CORE: A Foundation Model for Magnetic Resonance Imaging

The widespread use of Magnetic Resonance Imaging (MRI) in combination with deep learning shows promise for many high-impact automated diagnostic and prognostic tools. However, training new models requires large amounts of labeled data, a challenge due to high cost of precise annotations and data privacy. To address this issue, we introduce the MRI-CORE, a vision foundation model trained using more than 6 million slices from over 110 thousand MRI volumes across 18 body locations. Our experiments show notable improvements in performance over state-of-the-art methods in 13 data-restricted segmentation tasks, as well as in image classification, and zero-shot segmentation, showing the strong potential of MRI-CORE to enable data-efficient development of artificial intelligence models. We also present data on which strategies yield most useful foundation models and a novel analysis relating similarity between pre-training and downstream task data with transfer learning performance. Our model is publicly available with a permissive license. Magnetic Resonance Imaging (MRI) is one of the most widely used imaging modalities in medical diagnostics, with around 100-150 million scans performed annually worldwide (Papanicolas et al. 2018). MRI supports a wide range of clinical tasks, including lesion detection, tissue classification, and disease monitoring. Among these tasks, segmentation plays a particularly important role, as it enables precise delineation of anatomical structures and pathological regions, directly impacting diagnosis, treatment planning, and longitudinal studies (Mazurowski et al. 2023; Ma et al. 2024; Azad et al. 2024; Xu et al. 2024). Recent advances in deep learning have significantly improved the automation and accuracy of MRI-based analyses across a variety of tasks. However, deep learning-based methods typically require large amounts of manually annotated data and lack task transferability, making them difficult to scale across new tasks, anatomies, or patient populations.