Semi-supervised-learning-for-medical-image-segmentation.

• [New] We have transferred to a new topic about active learning and source-free domain adaptation for medical image analysis, which may be closer to the real clinical requirement. The new benchmark is here.

• We are reformatting the codebase to support the 5-fold cross-validation and randomly select labeled cases, the reformatted methods in this Branch.

• Recently, semi-supervised image segmentation has become a hot topic in medical image computing, unfortunately, there are only a few open-source codes and datasets, since the privacy policy and others. For easy evaluation and fair comparison, we are trying to build a semi-supervised medical image segmentation benchmark to boost the semi-supervised learning research in the medical image computing community. If you are interested, you can push your implementations or ideas to this repo or contact me at any time.

• This repo has re-implemented these semi-supervised methods (with some modifications for semi-supervised medical image segmentation, more details please refer to these original works): (1) Mean Teacher; (2) Entropy Minimization; (3) Deep Adversarial Networks; (4) Uncertainty Aware Mean Teacher; (5) Interpolation Consistency Training; (6) Uncertainty Rectified Pyramid Consistency; (7) Cross Pseudo Supervision; (8) Cross Consistency Training; (9) Deep Co-Training; (10) Cross Teaching between CNN and Transformer; (11) FixMatch; (12) Regularized Dropout. In addition, several backbones networks (both 2D and 3D) are also supported in this repo, such as UNet, nnUNet, VNet, AttentionUNet, ENet, Swin-UNet, etc.

• This project was originally developed for our previous works. Now and future, we are still working on extending it to be more user-friendly and support more approaches to further boost and ease this topic research. If you use this codebase in your research, please cite the following works:

    @article{media2022urpc,
    title={Semi-Supervised Medical Image Segmentation via Uncertainty Rectified Pyramid Consistency},
    author={Luo, Xiangde and Wang, Guotai and Liao, Wenjun and Chen, Jieneng and Song, Tao and Chen, Yinan and Zhang, Shichuan, Dimitris N. Metaxas, and Zhang, Shaoting},
    journal={Medical Image Analysis},
    volume={80},
    pages={102517},
    year={2022},
    publisher={Elsevier}}
    
    @inproceedings{luo2021ctbct,
    title={Semi-supervised medical image segmentation via cross teaching between cnn and transformer},
    author={Luo, Xiangde and Hu, Minhao and Song, Tao and Wang, Guotai and Zhang, Shaoting},
    booktitle={International Conference on Medical Imaging with Deep Learning},
    pages={820--833},
    year={2022},
    organization={PMLR}}
  
    @InProceedings{luo2021urpc,
    author={Luo, Xiangde and Liao, Wenjun and Chen, Jieneng and Song, Tao and Chen, Yinan and Zhang, Shichuan and Chen, Nianyong and Wang, Guotai and Zhang, Shaoting},
    title={Efficient Semi-supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency},
    booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2021},
    year={2021},
    pages={318--329}}
     
    @InProceedings{luo2021dtc,
    title={Semi-supervised Medical Image Segmentation through Dual-task Consistency},
    author={Luo, Xiangde and Chen, Jieneng and Song, Tao and  Wang, Guotai},
    journal={AAAI Conference on Artificial Intelligence},
    year={2021},
    pages={8801-8809}}
    
    @misc{ssl4mis2020,
    title={{SSL4MIS}},
    author={Luo, Xiangde},
    howpublished={\url{https://github.com/HiLab-git/SSL4MIS}},
    year={2020}}
    
  

Literature reviews of semi-supervised learning approach for medical image segmentation (SSL4MIS).

|Date|The First and Last Authors|Title|Code|Reference| |---|---|---|---|---| |2025-08|J. Zhu and H. Cui|CCA: Contrastive cluster assignment for supervised and semi-supervised medical image segmentation|Code|NN2025| |2025-04|Q. Zhou and Z. Wang|Single-slice Semi-supervised 3D Medical Image Segmentation via Correlation Information Enhancement and Hybrid Pseudo Mask Generation|None|JBHI2025| |2025-04|K. Yan and Z. Liu|SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image Segmentation|None|AAAI2025| |2025-04|H. Zhang and Y. Ma|Prototype-Augmented Mean Teacher for Robust Semi-Supervised Medical Image Segmentation|None|PR2025| |2025-04|W. Huang and L. Zhang|GapMatch: Bridging Instance and Model Perturbations for Enhanced Semi-Supervised Medical Image Segmentation|None|AAAI2025| |2025-04|Y. Wang and Y. Shi|Balancing Multi-Target Semi-Supervised Medical Image Segmentation with Collaborative Generalist and Specialists|Code|TMI2025| |2024-10|D, Abhijit and S. Roy|Confidence-Guided Semi-supervised Learning for Generalized Lesion Localization in X-Ray Images|Code|MICCAI2024| |2024-10|K. Eunjin and H. Park|Semi-supervised Segmentation through Rival Networks Collaboration with Saliency Map in Diabetic Retinopathy|Code|MICCAI2024| |2024-10|X. Liu and Y. Yuan|DiffRect: Latent Diffusion Label Rectification for Semi-supervised Medical Image Segmentation|Code|MICCAI2024| |2024-10|Z. Zhao and L. Wang|Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation|None|ECCV2024| |2024-09|B. Zhao and S. Ding|CrossMatch: Enhance Semi-Supervised Medical Image Segmentation with Perturbation Strategies and Knowledge Distillation|Code|JBHI2024| |2024-06|Z, Quan and X. Zhang|Robust Semi-Supervised 3D Medical Image Segmentation With Diverse Joint-Task Learning and Decoupled Inter-Student Learning|Code|TMI2024| |2024-06|Q. Ma and Y. Shi|Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation|Code|CVPR2024| |2024-06|H. Chi and B. Zhang|Adaptive Bidirectional Displacement for Semi-Supervised Medical Image Segmentation|Code|CVPR2024| |2024-05|J. Su and Z. Luo|Mutual learning with reliable pseudo label for semi-supervised medical image segmentation|Code|MedIA2024| |2024-01|Y. Ma and L. Wang|Model-Heterogeneous Semi-Supervised Federated Learning for Medical Image Segmentation|Code|TMI2024| |2023-10|Z. Wang and C. Ma|Dual-Contrastive Dual-Consistency Dual-Transformer: A Semi-Supervised Approach to Medical Image Segmentation|Code|ICCV2023| |2023-07|H. Wang and X. Li|DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation|Code|MICCAI2023| |2023-07|Q. Wei and Y. Zhou|Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation|Code|MICCAI2023| |2023-07|H. Peiris and M. Harandi|Uncertainty-guided dual-views for semi-supervised volumetric medical image segmentation|Code|[Nature Machine Intelligence](https://www.nature.com/articles/s42256-0