Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
660-04-006 ISMRM Abstract

Uncertainty-Weighted Consistency Learning for Semi-Supervised Medical Image Segmentation

Primary: Analysis Methods - Segmentation and Detection
Secondary: Acquisition & Reconstruction - AI methods
660-04-006 · AI: Anything Synthetic or Correcting Artifacts · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row A
Keywords: Deep learning Medical Image Segmentation Semi-Supervised Learning Uncertainty estimation
Accepted
Songyan Wu1,2, Zhengyong Huang1,3, Yao Sui 1,3,4
1National Institute of Health Data Science, Peking University, Beijing, China
2School of Mathematical Sciences, Beijing Normal University, Beijing, China
3Institute of Medical Technology, Peking University Health Science Center, Beijing, China
4Institute for Artificial Intelligence, Peking University, Beijing, China
Presenting Author: Yao Sui

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References

1. Butoi, V. I., Ortiz, J. J. G., Ma, T., Sabuncu, M. R., Guttag, J., & Dalca, A. V. (2023). Universeg: Universal medical image segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 21438-21451).
2. Azad, R., Aghdam, E. K., Rauland, A., Jia, Y., Avval, A. H., Bozorgpour, A., ... & Merhof, D. (2024). Medical image segmentation review: The success of u-net. IEEE Transactions on Pattern Analysis and Machine Intelligence.
3. Zhao, W., Zhong, L., Liao, X., Liao, W., Zhang, S., Zhang, S., & Wang, G. (2025). MetaSSL: A General Heterogeneous Loss for Semi-Supervised Medical Image Segmentation. IEEE Transactions on Medical Imaging.
4. Yu, L., Wang, S., Li, X., Fu, C. W., & Heng, P. A. (2019, October). Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In International conference on medical image computing and computer-assisted intervention (pp. 605-613). Cham: Springer International Publishing.
5. Wang, H., & Li, X. (2023, October). Dhc: Dual-debiased heterogeneous co-training framework for class-imbalanced semi-supervised medical image segmentation. In International conference on medical image computing and computer-assisted intervention (pp. 582-591). Cham: Springer Nature Switzerland.
6. Wang, Y., Xiao, B., Bi, X., Li, W., & Gao, X. (2023). Mcf: Mutual correction framework for semi-supervised medical image segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 15651-15660).
7. Tarvainen, A., & Valpola, H. (2017, July). Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In Advances in Neural Information Processing Systems (NeurIPS 2017), Workshop on Learning with Limited Labeled Data.
8. Milletari, F., Navab, N., & Ahmadi, S. A. (2016, October). V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV) (pp. 565-571). Ieee.
9. Kendall, A., & Gal, Y. (2017). What uncertainties do we need in Bayesian deep learning for computer vision? In Advances in Neural Information Processing Systems (NIPS 2017), pp. 5574–5584.
10. Laine, S., & Aila, T. (2017, April). Temporal ensembling for semi-supervised learning. In International Conference on Learning Representations (ICLR 2017), Workshop Track.
11. Wu, Y., Xu, M., Ge, Z., Cai, J., & Zhang, L. (2021, September). Semi-supervised left atrium segmentation with mutual consistency training. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 297-306). Cham: Springer International Publishing.
12. Huang, Z., & Sui, Y. (2024, October). Contour-weighted loss for class-imbalanced image segmentation. In 2024 IEEE International Conference on Image Processing (ICIP) (pp. 3084-3090). IEEE.
13. Kumar, A., Mitra, S., & Rawat, Y. S. (2025, April). Stable mean teacher for semi-supervised video action detection. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 4, pp. 4419-4427).
14. Zhang, Y., Zhang, J., & Wang, Y. (2022). Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation. arXiv:2203. Dual-task mutual learning leverages two complementary tasks with mutual consistency to improve semi-supervised segmentation performance.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/660-04-006