MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
Journal:
arXiv
Published Date:
Jun 13, 2025
Abstract
The widespread use of Magnetic Resonance Imaging (MRI) and the rise of deep
learning have enabled the development of powerful predictive models for a wide
range of diagnostic tasks in MRI, such as image classification or object
segmentation. However, training models for specific new tasks often requires
large amounts of labeled data, which is difficult to obtain due to high
annotation costs and data privacy concerns. To circumvent this issue, we
introduce MRI-CORE (MRI COmprehensive Representation Encoder), a vision
foundation model pre-trained using more than 6 million slices from over 110,000
MRI volumes across 18 main body locations. Experiments on five diverse object
segmentation tasks in MRI demonstrate that MRI-CORE can significantly improve
segmentation performance in realistic scenarios with limited labeled data
availability, achieving an average gain of 6.97% 3D Dice Coefficient using only
10 annotated slices per task. We further demonstrate new model capabilities in
MRI such as classification of image properties including body location,
sequence type and institution, and zero-shot segmentation. These results
highlight the value of MRI-CORE as a generalist vision foundation model for
MRI, potentially lowering the data annotation resource barriers for many
applications.
