Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
468-03-008 ISMRM Abstract

Foundation Model-Assisted Robust Semi-Supervised Learning for Cardiac MRI Segmentation

Primary: Analysis Methods - Segmentation and Detection
Secondary: Analysis Methods - Foundation Models
468-03-008 · Segmentation for Cardiac Applications · Tuesday, 12 May, 1:40 PM–2:35 PM · Digital Posters Row I
Keywords: Foundation model Deep Learning Segmentation Semi-Supervised Learning Segment anything model
Accepted
Rui Zhang1, Zijian Zhou 1, Yuan Feng2,3,4, Yining Wang5, Peng Hu1,6
1School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
2Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
3National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai 200240, China
4School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
5Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, China
6State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
Presenting Author: Zijian Zhou

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References

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3. Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo W-Y Segment anything. In: Proceedings of the IEEE/CVF international conference on computer vision, 2023. pp 4015-4026.
4. Tarvainen A, Valpola H (2017) Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems 30.
5. Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng P-A, Cetin I, Lekadir K, Camara O, Ballester MAG (2018) Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging 37 (11):2514-2525.
6. Ronneberger O, Fischer P, Brox T U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, 2015. Springer, pp 234-241.
7. Wu Y, Xu M, Ge Z, Cai J, Zhang L Semi-supervised left atrium segmentation with mutual consistency training. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021. Springer, pp 297-306.
8. Zhang Y, Zhou T, Wang S, Wu Y, Gu P, Chen DZ (2023) SAMDSK: Combining segment anything model with domain-specific knowledge for semi-supervised learning in medical image segmentation. arXiv preprint arXiv:230813759.
9. Yang D, Ji J, Ma Y, Guo T, Wang H, Sun X, Ji R (2024) Sam as the guide: Mastering pseudo-label refinement in semi-supervised referring expression segmentation. arXiv preprint arXiv:240601451.
10. Houlsby N, Giurgiu A, Jastrzebski S, Morrone B, De Laroussilhe Q, Gesmundo A, Attariyan M, Gelly S Parameter-efficient transfer learning for NLP. In: International conference on machine learning, 2019. PMLR, pp 2790-2799.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/468-03-008