Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
401-02-003 ISMRM Abstract

BrainMR Specialist: A Foundation Model of Brain MRI for Diverse Downstream Applications

Primary: Analysis Methods - Foundation Models
Secondary: Analysis Methods - Multi-Modal Learning with LLMs/VLMs
401-02-003 · Foundation Models · Tuesday, 12 May, 8:20 AM–10:10 AM · Hall 1A
Keywords: Machine Learning/Artificial Intelligence Foundation model Multimodal Large Language Models
Accepted
Juhyung Park 1, Minjun Kim1, Rokgi Hong1, Jaehyeon Koo1, Roh-Eul Yoo2,3, Seung Hong Choi2,3, Jongho Lee1
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of
2Department of Radiology, Seoul National University Hospital, Seoul, Korea, Republic of
3Department of Radiology, Seoul National University College of Medicine, Seoul, Korea, Republic of
Presenting Author: Juhyung Park

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. X. He, et al., “OmniMRI: A Unified Vision–Language Foundation Model for Generalist MRI Interpretation,” arXiv preprint arXiv:2508.17524, 2025. doi:10.48550/arXiv.2508.17524 [doi]
2. Z. Qiu, et al., “Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications,” arXiv preprint arXiv:2508.07165, 2025. doi:10.48550/arXiv.2508.07165 [doi]
3. S. Wang, et al., “Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging,” arXiv preprint arXiv:2502.14064, 2025. doi:10.48550/arXiv.2502.14064 [doi]
4. H. Dong, et al., “MRI-CORE: A Foundation Model for Magnetic Resonance Imaging,” arXiv preprint arXiv:2506.12186, 2025. doi:10.48550/arXiv.2506.12186 [doi]
5. L. Shao, et al., “An MRI-pathology Foundation Model for Noninvasive Diagnosis and Grading of Prostate Cancer,” Nat. Cancer, vol. 6, pp. 1621–1637, 2025. doi:10.1038/s43018-025-01041-x [doi]
6. X. Zhang, et al., “A Foundation Model for Lesion Segmentation on Brain MRI With Mixture of Modality Experts,” IEEE Trans. Med. Imag., vol. 44, no. 6, 2025. doi:10.1109/TMI.2025.3540809 [doi]
7. S. Rui, et al., “Multi-modal Vision Pre-training for Medical Image Analysis,” arXiv preprint arXiv:2410.10604, 2024. doi:10.48550/arXiv.2410.10604 [doi]
8. J. Zbontar, et al., “fastMRI: An open dataset and benchmarks for accelerated MRI,” arXiv preprint arXiv:1811.08839, 2018. doi:10.48550/arXiv.1811.08839 [doi]
9. P. J. LaMontagne, et al., “OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease,” medRxiv, 2019. doi:10.1101/2019.12.13.19014902 [doi]
10. U. Baid, et al., “The RSNA-ASNR-MICCAI BraTS 2021 Benchmark of Brain Tumor Segmentation and Radiogenomic Classification,” arXiv preprint arXiv:2107.02314, 2021. doi:10.48550/arXiv.2107.02314 [doi]
11. B.H. Menze, et al., “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS),” IEEE Trans. Med. Imag., vol. 34, no. 10, pp. 1993–2024, 2015. doi:10.1109/TMI.2014.2377694 [doi]
12. S. Bakas, et al., “Advancing The Cancer Genome Atlas Glioma MRI Collections with Expert Segmentation Labels and Radiomic Features,” Sci. Data, vol. 4, 170117, 2017. doi:10.1038/sdata.2017.117 [doi]
13. “IXI Dataset,” Information eXtraction from Images (EPSRC GR/S21533/02). Available online: http://brain-development.org/ixi-dataset
14. R.C. Petersen, et al., “Alzheimer’s Disease Neuroimaging Initiative (ADNI): Clinical Characterization,” Neurology, vol. 74, no. 3, pp. 201–209, 2010. doi:10.1212/WNL.0b013e3181cb3e25 [doi]
15. J. Lei, et al., “AutoRG-Brain: Grounded Report Generation for Brain MRI,” arXiv preprint arXiv:2407.16684, 2024. doi:10.48550/arXiv.2407.16684 [doi]
16. A. Radford, et al., “Learning Transferable Visual Models from Natural Language Supervision,” in Proc. Int. Conf. Mach. Learn. (ICML), 2021, pp. 8748–8763.
17. T. Zhang, et al., “BERTScore: Evaluating Text Generation with BERT,” arXiv preprint arXiv:1904.09675, 2019. doi:10.48550/arXiv.1904.09675 [doi]
18. J. Yu, et al., “CoCa: Contrastive Captioners are Image-Text Foundation Models,” arXiv preprint arXiv:2205.01917, 2022. doi:10.48550/arXiv.2205.01917 [doi]
19. O. Ronneberger, et al., "U-Net: Convolutional Networks for Biomedical Image Segmentation," in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Interv. (MICCAI), 2015, pp. 234–241. doi:10.1007/978-3-319-24574-4_28 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/401-02-003