Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
401-02-010 ISMRM Abstract

Self-Supervised Representation Learning of Brain MRI Using DINOv3: From Anatomical Features to Cross-Domain Generalization

Primary: Acquisition & Reconstruction - AI methods
Secondary: Analysis Methods - Segmentation and Detection
401-02-010 · Foundation Models · Tuesday, 12 May, 8:20 AM–10:10 AM · Hall 1A
Keywords: Self-supervised learning Lesion segmentation Skull-stripping DINOv3 Brain MRI
Accepted
Wei Jiang 1, Tianyi Ding1,2, Hongli Chen2, Wenjia Song2, Zhuang Xiong3, Yang Gao4, Nan Ye1, Feng Liu1, Hongfu Sun5
1The University of Queensland, Brisbane, Australia
2School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
3Image X Institute, The University of Sydney, Sydney, Australia
4School of Computer Science and Engineering, Central South University, Changsha, China
5University of Newcastle, Newcastle, Australia
Presenting Author: Wei Jiang

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References

1. Caron, Mathilde, et al. "Emerging properties in self-supervised vision transformers." Proceedings of the IEEE/CVF international conference on computer vision. 2021. https://doi.org/10.48550/arXiv.2104.14294 [doi]
2. Oquab, Maxime, et al. "Dinov2: Learning robust visual features without supervision." arXiv preprint (2023). https://doi.org/10.48550/arXiv.2304.07193 [doi]
3. Siméoni, Oriane, et al. "Dinov3." arXiv preprint (2025). https://doi.org/10.48550/arXiv.2508.10104 [doi]
4. Zhou, Jinghao, et al. "ibot: Image bert pre-training with online tokenizer." arXiv preprint (2021). https://doi.org/10.48550/arXiv.2111.07832 [doi]
5. Brown, Tom, et al. "Language models are few-shot learners." Advances in neural information processing systems 33 (2020): 1877-1901. https://doi.org/10.48550/arXiv.2005.14165 [doi]
6. Dosovitskiy, Alexey. "An image is worth 16x16 words: Transformers for image recognition at scale." arXiv preprint arXiv:2010.11929 (2020). https://doi.org/10.48550/arXiv.2010.11929 [doi]
7. Zbontar, Jure, et al. "fastMRI: An open dataset and benchmarks for accelerated MRI." arXiv preprint (2018). https://doi.org/10.48550/arXiv.1811.08839 [doi]
8. Menze, Bjoern H., et al. "The multimodal brain tumor image segmentation benchmark (BRATS)." IEEE transactions on medical imaging 34.10 (2014): 1993-2024. https://doi.org/10.1109/TMI.2014.2377694 [doi]

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http://echo.ismrm.org/p/ISMRM2026/401-02-010