mlsanityData to 14 Oct 2026

MRI-CORE: A Foundation Model for Magnetic Resonance Imaging

arXiv 2506.12186 ↗First posted 13 Jun 2025eess.IV cs.AI cs.CVcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 21 of its 36 references that are in the map. First posted on arXiv 13 Jun 2025.

Builds on

21 of 36 references.

References · 21

  1. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  2. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  3. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  4. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,750
  5. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
  6. 2018Demystifying MMD GANscited by 1,293
  7. 2021iBOT: Image BERT Pre-Training with Online Tokenizercited by 903
  8. 2023Segment Anything Model for Medical Image Analysis: an Experimental Studycited by 291
  9. 2022Medical Image Segmentation Review: The success of U-Netcited by 191
  10. 2023Virchow: A Million-Slide Digital Pathology Foundation Modelcited by 90
  11. 2024How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Modelcited by 32
  12. 2023Lumbar spine segmentation in MR images: a dataset and a public benchmarkcited by 30
9 more
  1. 2023Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trendcited by 24
  2. 2025Triad: Vision Foundation Model for 3D Magnetic Resonance Imagingcited by 24
  3. 2024Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasetscited by 19
  4. 2021Data synthesis and adversarial networks: A review and meta-analysis in cancer imagingcited by 15
  5. 2021PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imagingcited by 14
  6. 2024SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRIcited by 14
  7. 2024Large Scale MRI Collection and Segmentation of Cirrhotic Livercited by 10
  8. 2025A Pragmatic Note on Evaluating Generative Models with Fr'echet Inception Distance for Retinal Image Synthesiscited by 9
  9. 2024Rethinking Perceptual Metrics for Medical Image Translationcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 8.

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  1. 2025Revisiting 2D Foundation Models for Scalable 3D Medical Image Classificationcited by 9
  2. 2025Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?cited by 6
  3. 2025CLARITY: Medical World Model for Guiding Treatment Decisions by Simulating Context-Aware Disease Trajectoriescited by 6
  4. 2025On the Utility of Foundation Models for Fast MRI: Vision-Language-Guided Image Reconstructioncited by 4
  5. 2025Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applicationscited by 3
  6. 2025PROFUSEme: PROstate Cancer Biochemical Recurrence Prediction via FUSEd Multi-modal Embeddingscited by 2
  7. 2026Masked and Predictive Self-Supervised Foundation Models for 3D Brain MRIcited by 1
  8. 2025Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentationcited by 0
  9. 2026A Calibrated Memorization Index (MI) for Detecting Training Data Leakage in Generative MRI Modelscited by 0
  10. 2026LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantificationcited by 0
  11. 2026How Much MRI Preprocessing Is Enough? A Cost-Utility Study for Brain MRI Foundation Modelscited by 0
  12. 2026CHIMERA Challenge: Biochemical Recurrence Prediction in Prostate Cancer Patients using multimodal datasetscited by 0
3 more
  1. 2026Med-RADIO: Reducing All Medical Domains Into One via Multi-Teacher Distillationcited by 0
  2. 2026WASP: Weakly Aligned Spatiotemporal Pairs for Fetal Brain MRI-Ultrasound Learningcited by 0
  3. 2026A4-MR: Abnormality-aware Anatomical Alignment for MRcited by 0