# MRI-CORE: A Foundation Model for Magnetic Resonance Imaging

arXiv: https://arxiv.org/abs/2506.12186 · HTML: https://mlsanity.com/paper/2506.12186/

## 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 (most cited first, 21 of 21)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [iBOT: Image BERT Pre-Training with Online Tokenizer](https://mlsanity.com/paper/2111.07832/) (2021)
- [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023)
- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [Virchow: A Million-Slide Digital Pathology Foundation Model](https://mlsanity.com/paper/2309.07778/) (2023)
- [How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model](https://mlsanity.com/paper/2404.09957/) (2024)
- [Lumbar spine segmentation in MR images: a dataset and a public benchmark](https://mlsanity.com/paper/2306.12217/) (2023)
- [Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend](https://mlsanity.com/paper/2311.13717/) (2023)
- [Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging](https://mlsanity.com/paper/2502.14064/) (2025)
- [Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets](https://mlsanity.com/paper/2412.01496/) (2024)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging](https://mlsanity.com/paper/2106.10893/) (2021)
- [SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI](https://mlsanity.com/paper/2401.12974/) (2024)
- [Large Scale MRI Collection and Segmentation of Cirrhotic Liver](https://mlsanity.com/paper/2410.16296/) (2024)
- [A Pragmatic Note on Evaluating Generative Models with Fr'echet Inception Distance for Retinal Image Synthesis](https://mlsanity.com/paper/2502.17160/) (2025)
- [Rethinking Perceptual Metrics for Medical Image Translation](https://mlsanity.com/paper/2404.07318/) (2024)

## Built on by (most cited first, 15 of 15)

- [Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification](https://mlsanity.com/paper/2512.12887/) (2025)
- [Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?](https://mlsanity.com/paper/2507.11569/) (2025)
- [CLARITY: Medical World Model for Guiding Treatment Decisions by Simulating Context-Aware Disease Trajectories](https://mlsanity.com/paper/2512.08029/) (2025)
- [On the Utility of Foundation Models for Fast MRI: Vision-Language-Guided Image Reconstruction](https://mlsanity.com/paper/2511.19641/) (2025)
- [Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications](https://mlsanity.com/paper/2508.07165/) (2025)
- [PROFUSEme: PROstate Cancer Biochemical Recurrence Prediction via FUSEd Multi-modal Embeddings](https://mlsanity.com/paper/2509.14051/) (2025)
- [Masked and Predictive Self-Supervised Foundation Models for 3D Brain MRI](https://mlsanity.com/paper/2606.13315/) (2026)
- [Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation](https://mlsanity.com/paper/2507.17971/) (2025)
- [A Calibrated Memorization Index (MI) for Detecting Training Data Leakage in Generative MRI Models](https://mlsanity.com/paper/2602.13066/) (2026)
- [LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification](https://mlsanity.com/paper/2605.30829/) (2026)
- [How Much MRI Preprocessing Is Enough? A Cost-Utility Study for Brain MRI Foundation Models](https://mlsanity.com/paper/2606.08164/) (2026)
- [CHIMERA Challenge: Biochemical Recurrence Prediction in Prostate Cancer Patients using multimodal datasets](https://mlsanity.com/paper/2608.21497/) (2026)
- [Med-RADIO: Reducing All Medical Domains Into One via Multi-Teacher Distillation](https://mlsanity.com/paper/2609.37682/) (2026)
- [WASP: Weakly Aligned Spatiotemporal Pairs for Fetal Brain MRI-Ultrasound Learning](https://mlsanity.com/paper/2610.04601/) (2026)
- [A4-MR: Abnormality-aware Anatomical Alignment for MR](https://mlsanity.com/paper/2610.13056/) (2026)

## 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.