MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908461709656064 |
|---|---|
| author | Dong, Haoyu Chen, Yuwen Gu, Hanxue Konz, Nicholas Chen, Yaqian Li, Qihang Mazurowski, Maciej A. |
| author_facet | Dong, Haoyu Chen, Yuwen Gu, Hanxue Konz, Nicholas Chen, Yaqian Li, Qihang Mazurowski, Maciej A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12186 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MRI-CORE: A Foundation Model for Magnetic Resonance Imaging Dong, Haoyu Chen, Yuwen Gu, Hanxue Konz, Nicholas Chen, Yaqian Li, Qihang Mazurowski, Maciej A. Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 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. |
| title | MRI-CORE: A Foundation Model for Magnetic Resonance Imaging |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2506.12186 |