Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
331-02-013 / 331-02-013 ISMRM Abstract

Application of Multi-Teacher Distillation for Enhancing the Effectiveness of Medical Image Segmentation via Foundation Models

Primary: Cross-Organ
Secondary: Analysis Methods - Segmentation and Detection
331-02-013 · AI Frontiers in Image Analysis · Monday, 11 May, 1:50 PM–3:06 PM · Roof Terrace
331-02-013 · AI Frontiers in Image Analysis · Monday, 11 May, 1:50 PM–3:06 PM · Roof Terrace
Keywords: Machine Learning/Artificial Intelligence Segmentation Deep learning AI Foundation Model
Accepted
Qing Li 1, Yizhe Zhang2, Ying-Hua Chu3, Mo Yang1, Haoyang Zhang1, Junhong Liu1, Wang Liao1, Shuo Wang4, Chengyan Wang1
1Human Phenome Institute, Fudan University, Shanghai, China
2School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
3MR Research Collaboration Team, Shanghai, China
4Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
Presenting Author: Qing Li

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References

1. Kirillov A, Mintun E, Ravi N, et al. Segment anything[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2023: 4015-4026.
2. Li Q, Zhang Y, Li Y, et al. An empirical study on the fairness of foundation models for multi-organ image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2024: 432-442.
3. Zhang H, Chen D, Wang C. Confidence-aware multi-teacher knowledge distillation[C]//ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022: 4498-4502.
4. Ravi N, Gabeur V, Hu Y T, et al. Sam 2: Segment anything in images and videos[J]. arXiv preprint arXiv:2408.00714, 2024.
5. Ma J, He Y, Li F, et al. Segment anything in medical images[J]. Nature Communications, 2024, 15(1): 654.
6. Zhu J, Hamdi A, Qi Y, et al. Medical sam 2: Segment medical images as video via segment anything model 2[J]. arXiv preprint arXiv:2408.00874, 2024.
7. Zhao Z, Zhang Y, Wu C, et al. One model to rule them all: Towards universal segmentation for medical images with text prompts[J]. arXiv preprint arXiv:2312.17183, 2023.
8. Wasserthal J, Breit H C, Meyer M T, et al. TotalSegmentator: robust segmentation of 104 anatomic structures in CT images[J]. Radiology: Artificial Intelligence, 2023, 5(5): e230024.
9. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]//International Conference on Medical image computing and computer-assisted intervention. Cham: Springer international publishing, 2015: 234-241.
10. Zhang W, Fu C, Zheng Y, et al. HSNet: A hybrid semantic network for polyp segmentation[J]. Computers in biology and medicine, 2022, 150: 106173.

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http://echo.ismrm.org/p/ISMRM2026/331-02-013