# SSL4MIS

**Repository Path**: nosferatu/SSL4MIS

## Basic Information

- **Project Name**: SSL4MIS
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-07-22
- **Last Updated**: 2024-07-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](https://github.com/whq-xxh/ADA4MIA).**
* We are reformatting the codebase to support the 5-fold cross-validation and randomly select labeled cases, the reformatted methods in this [Branch](https://github.com/HiLab-git/SSL4MIS/tree/cross_val_dev). 
* 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](https://luoxd1996.github.io/) 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](https://papers.nips.cc/paper/6719-mean-teachers-are-better-role-models-weight-averaged-consistency-targets-improve-semi-supervised-deep-learning-results.pdf); (2) [Entropy Minimization](https://openaccess.thecvf.com/content_CVPR_2019/papers/Vu_ADVENT_Adversarial_Entropy_Minimization_for_Domain_Adaptation_in_Semantic_Segmentation_CVPR_2019_paper.pdf); (3) [Deep Adversarial Networks](https://link.springer.com/chapter/10.1007/978-3-319-66179-7_47); (4) [Uncertainty Aware Mean Teacher](https://arxiv.org/pdf/1907.07034.pdf); (5) [Interpolation Consistency Training](https://arxiv.org/pdf/1903.03825.pdf); (6) [Uncertainty Rectified Pyramid Consistency](https://arxiv.org/pdf/2012.07042.pdf); (7) [Cross Pseudo Supervision](https://arxiv.org/abs/2106.01226); (8) [Cross Consistency Training](https://openaccess.thecvf.com/content_CVPR_2020/papers/Ouali_Semi-Supervised_Semantic_Segmentation_With_Cross-Consistency_Training_CVPR_2020_paper.pdf); (9) [Deep Co-Training](https://openaccess.thecvf.com/content_ECCV_2018/papers/Siyuan_Qiao_Deep_Co-Training_for_ECCV_2018_paper.pdf); (10) [Cross Teaching between CNN and Transformer](https://arxiv.org/pdf/2112.04894.pdf); (11) [FixMatch](https://arxiv.org/abs/2001.07685); (12) [Regularized Dropout](https://proceedings.neurips.cc/paper/2021/file/5a66b9200f29ac3fa0ae244cc2a51b39-Paper.pdf). 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|
|---|---|---|---|---|
|2023-07|H. Wang and X. Li|DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation|[Code](https://github.com/xmed-lab/DHC)|[MICCAI2023](https://arxiv.org/pdf/2307.11960.pdf)|
|2023-07|Q. Wei and Y. Zhou|Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation|[Code](https://github.com/aijinrjinr/MLB-Seg)|[MICCAI2023](https://arxiv.org/pdf/2307.11604.pdf)|
|2023-07|H. Peiris and M. Harandi|Uncertainty-guided dual-views for semi-supervised volumetric medical image segmentation|[Code](https://github.com/himashi92/Co-BioNet)|[Nature Machine Intelligence](https://www.nature.com/articles/s42256-023-00682-w)|
|2023-07|S. Gao and S. Zhang|Correlation-Aware Mutual Learning for Semi-supervised Medical Image Segmentation|[Code](https://github.com/Herschel555/CAML)|[MICCAI2023](https://arxiv.org/pdf/2307.06312.pdf)|
|2023-07|Z. Xu and R. Tong|Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation|[Code](https://github.com/lemoshu/AC-MT)|[MedIA2023](https://www.sciencedirect.com/science/article/pii/S1361841523001408)|
|2023-06|P. Liu and G. Zheng|C3PS: Context-aware Conditional Cross Pseudo Supervision for Semi-supervised Medical Image Segmentation|None|[Arxiv](https://arxiv.org/pdf/2306.08275.pdf)|
|2023-05|Z. Zhang and Z. Jiao|Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation|None|[Arxiv](https://arxiv.org/pdf/2305.16216.pdf)|
|2023-05|Z. Zhang and Z. Jiao|Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation|None|[MICCAI2023](https://arxiv.org/pdf/2305.16214.pdf)|
|2023-05|H. Cai and Y. Gao|Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation|[Code](https://github.com/HengCai-NJU/DeSCO)|[CVPR2023](https://openaccess.thecvf.com/content/CVPR2023/papers/Cai_Orthogonal_Annotation_Benefits_Barely-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.pdf)|
|2023-05|H. Basak and Z. yin|Pseudo-Label Guided Contrastive Learning for Semi-Supervised Medical Image Segmentation|[Code](https://github.com/hritam-98/PatchCL-MedSeg)|[CVPR2023](https://openaccess.thecvf.com/content/CVPR2023/papers/Basak_Pseudo-Label_Guided_Contrastive_Learning_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.pdf)|
|2023-05|Y. Wang and X. Gao|MCF: Mutual Correction Framework for Semi-Supervised Medical Image Segmentation|[Code](https://github.com/WYC-321/MCF)|[CVPR2023](https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_MCF_Mutual_Correction_Framework_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.pdf)|
|2023-05|L. Zhong and G. Wang|Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions|[Code](https://github.com/HiLab-git/CDMA)|[MICCAI2023](https://arxiv.org/pdf/2305.18830.pdf)|
|2023-05|J. Du and T. Wang|Coarse-Refined Consistency Learning using Pixel-level Features for Semi-supervised Medical Image Segmentation|None|[JBHI2023](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10131969)|
|2023-05|L. Qiu and H. Ren|Federated Semi-Supervised Learning for Medical Image Segmentation via Pseudo-Label Denoising|None|[JBHI2023](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10121665)|
|2023-05|R. Aralikatti and J. Rajan|A Dual-Stage Semi-Supervised Pre-Training Approach for Medical Image Segmentation|[Code](https://github.com/RajathCA/Dual-Stage-Semi-Supervised-Pre-Training)|[TAI2023](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10114995)|
|2023-05|Y. Zhao and J. Lu|Semi-Supervised Medical Image Segmentation With Voxel Stability and Reliability Constraints|[Code](https://github.com/zyvcks/JBHI-VSRC)|[JBHI2023](https://ieeexplore.ieee.org/abstract/document/10120761)|
|2023-05|M. Xu and J. Jacob|Expectation Maximization Pseudo Labelling for Segmentation with Limited Annotations|[Code](https://github.com/moucheng2017/EMSSL)|[Arxiv](https://arxiv.org/pdf/2305.01747.pdf)|
|2023-05|Y. Bai and Y. Wang|Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation|[Code](https://github.com/DeepMed-Lab-ECNU/BCP)|[CVPR2023](https://arxiv.org/pdf/2305.00673.pdf)|
|2023-04|H. Wu and K. Cheng|Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation|[Code](https://github.com/Huiimin5/comwin)|[Arixv](https://arxiv.org/pdf/2304.07519.pdf)|
|2023-04|A. Lou and J. Noble|Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation|[Code](https://github.com/AngeLouCN/Min_Max_Similarity)|[TMI2023](https://arxiv.org/ftp/arxiv/papers/2203/2203.15177.pdf)|
|2023-04|C.You and J. Duncan|ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast|None|[Arxiv](https://arxiv.org/abs/2304.02689)|
|2023-03|Y. Zhu and R. Zhang|Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation|[Code](https://github.com/zhuye98/ICL)|[MIDL2023](https://arxiv.org/pdf/2303.14175.pdf)|
|2023-03|C. Xu and S. Zhao|Dual Uncertainty-guided Mixing Consistency for Semi-Supervised 3D Medical Image Segmentation|[Code](https://github.com/yang6277/DUMC)|[TBD2023](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10075508)|
|2023-03|K. Chaitanya and E. Konukoglu|Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation|[Code](https://github.com/krishnabits001/pseudo_label_contrastive_training)|[MedIA2023](https://www.sciencedirect.com/science/article/pii/S1361841523000531?via%3Dihub)|
|2023-03|J. Zhu and E. Meijering|Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans|[Code](https://github.com/ThisGame42/Hybrid-Teacher)|[Arxiv](https://arxiv.org/pdf/2303.05126.pdf)|
|2023-02|P. Wang and C. Desrosiers|CAT: Constrained Adversarial Training for Anatomically-plausible Semi-supervised Segmentation|[Code](https://github.com/WangPing521/constraint_aware_vat_semi_supervised_segmentation)|[TMI2023](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10038734)|
|2023-02|C. You and J. Duncan|Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective|None|[Arxiv](https://arxiv.org/pdf/2302.01735.pdf)|
|2023-02|L. Zeng and W. Wang|SS-TBN: A Semi-Supervised Tri-Branch Network for COVID-19 Screening and Lesion Segmentation|None|[TPAMI2023](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10032636)|
|2023-01|X. Zhao and L. Zhang|RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation|[Code](https://github.com/hsiangyuzhao/RCPS)|[Arxiv](https://arxiv.org/abs/2301.05500)|
|2023-01|Z. Shen and O. Zaiane|Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation|[Code](https://github.com/Senyh/UCMT)|[Arxiv](https://arxiv.org/pdf/2301.04465.pdf)|
|2023-01|D. Chen and Y. Wang|MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery|None|[CVPR2023](https://arxiv.org/pdf/2212.14310.pdf)|
|2022-12|P. Qiao and J. Chen|Semi-supervised CT Lesion Segmentation Using Uncertainty-based Data Pairing and SwapMix|None|[TMI2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10002838)|
|2022-12|Z. Wang and Z. Ni|Adversarial Vision Transformer for Medical Image Semantic Segmentation with Limited Annotations|[Code](https://github.com/ziyangwang007/CV-SSL-MIS)|[BMVC2022](https://bmvc2022.mpi-inf.mpg.de/1002.pdf)|
|2022-12|T. Lei and A. Nandi|Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network|[Code](https://github.com/SUST-reynole/ASE-Net)|[TMI2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9966841)|
|2022-11|L. Wang and P. Heng|Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19|[Code](https://github.com/jcwang123/DM2TNet)|[TCYB2022](https://arxiv.org/pdf/2211.05548.pdf)|
|2022-10|H. Ni and X. Huang|Semi-supervised Body Parsing and Pose Estimation for Enhancing Infant General Movement Assessment|None|[MedIA2023](https://arxiv.org/abs/2210.08054)|
|2022-10|F. Fyu and P. Yuen|Pseudo-Label Guided Image Synthesis for Semi-Supervised COVID-19 Pneumonia Infection Segmentation|[Code](https://github.com/FeiLyu/SASSL)|[TMI2022](https://ieeexplore.ieee.org/document/9931157)|
|2022-10|J. Shi and C. Li|Semi-Supervised Pixel Contrastive Learning Framework for Tissue Segmentation in Histopathological Image|None|[JBHI2022](https://ieeexplore.ieee.org/document/9926096)|
|2022-10|C. Xu and S. Li|BMAnet: Boundary Mining with Adversarial Learning for Semi-supervised 2D Myocardial Infarction Segmentation|None|[JBHI2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9924606)|
|2022-10|D. Xiang and B. Tian|Semi-supervised Dual Stream Segmentation Network for Fundus Lesion Segmentation|None|[TMI2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9924191)|
|2022-10|F. Wu and X. Zhuang|Minimizing Estimated Risks on Unlabeled Data: A New Formulation for Semi-Supervised Medical Image Segmentation|[Code](https://zmiclab.github.io/projects.html)|[TPAMI2022](https://ieeexplore.ieee.org/document/9921323)|
|2022-10|S. Zhang and Z. Xu|Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation|None|[MedIA2022](https://www.sciencedirect.com/science/article/pii/S1361841522002845#!)|
|2022-10|J. Chen and J. Han|Semi-supervised Unpaired Medical Image Segmentation Through Task-affinity Consistency|[Code](https://github.com/jingkunchen/TAC)|[TMI2022](https://ieeexplore.ieee.org/document/9915650)|
|2022-09|H. Huang and Y. Zou|Complementary consistency semi-supervised learning for 3D left atrial image segmentation|[Code](https://github.com/Cuthbert-Huang/CC-Net)|[Arxiv](https://arxiv.org/pdf/2210.01438.pdf)|
|2022-09|R. Gu and S. Zhang|Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures|[Code](https://github.com/HiLab-git/DAG4MIA)|[TMI2022](https://ieeexplore.ieee.org/document/9903480)|
|2022-09|Q. Jin and R. Su|Semi-supervised Histological Image Segmentation via Hierarchical Consistency Enforcement|None|[MICCAI2022](https://link.springer.com/chapter/10.1007/978-3-031-16434-7_1)|
|2022-09|J. Xiang and Y. Yang|FUSSNet: Fusing Two Sources of Uncertainty for Semi-supervised Medical Image Segmentation|[Code](https://github.com/grant-jpg/FUSSNet)|[MICCAI2022](https://link.springer.com/chapter/10.1007/978-3-031-16452-1_46)|
|2022-09|V. Nath and D. Xu|Warm Start Active Learning with Proxy Labels and Selection via Semi-supervised Fine-Tuning|None|[MICCAI2022](https://link.springer.com/chapter/10.1007/978-3-031-16452-1_29)|
|2022-09|J. Liu and Y. Zhou|Semi-supervised Medical Image Segmentation Using Cross-Model Pseudo-Supervision with Shape Awareness and Local Context Constraints|[Code](https://github.com/igip-liu/SLC-Net)|[MICCAI2022](https://link.springer.com/chapter/10.1007/978-3-031-16452-1_14)|
|2022-09|Y. Meng and Y. Zheng|Shape-Aware Weakly/Semi-Supervised Optic Disc and Cup Segmentation with Regional/Marginal Consistency|[Code](https://github.com/smallmax00/Share_aware_Weakly-Semi_ODOC_seg)|[MICCAI2022](https://link.springer.com/chapter/10.1007/978-3-031-16440-8_50#auth-Yanda-Meng)|
|2022-09|X. Zhao and G. Li|Semi-supervised Spatial Temporal Attention Network for Video Polyp Segmentation|[Code](https://github.com/ShinkaiZ/SSTAN)|[MICCAI2022](https://link.springer.com/chapter/10.1007/978-3-031-16440-8_44)|
|2022-09|J. Wu and D. Ding|Semi-supervised Learning for Nerve Segmentation in Corneal Confocal Microscope Photography|None|[MICCAI2022](https://link.springer.com/chapter/10.1007/978-3-031-16440-8_5)|
|2022-09|H. Basak and R. Sarkar|Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI|None|[MICCAI2022](https://arxiv.org/pdf/2209.00123.pdf)|
|2022-08|Q. Wang and J. Chen|A regularization-driven Mean Teacher model based on semi-supervised learning for medical image segmentation|[Code](https://github.com/qingwang-usc/RMT_VAT)|[PMB2022](https://iopscience.iop.org/article/10.1088/1361-6560/ac89c8)|
|2022-08|Y. Meng and Y. Zheng|Dual Consistency Enabled Weakly and Semi-Supervised Optic Disc and Cup Segmentation with Dual Adaptive Graph Convolutional Networks|[Code](https://github.com/smallmax00/Dual_Adaptive_Graph_Reasoning)|[TMI2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9870838)|
|2022-08|T. Lei and B. Lu|Semi-Supervised 3D Medical Image Segmentation Using Shape-Guided Dual Consistency Learning|None|[ICME2022](https://www.computer.org/csdl/proceedings-article/icme/2022/09859611/1G9ERIbVbOg)|
|2022-08|J. Chen and C. Sham|Uncertainty teacher with dense focal loss for semi-supervised medical image segmentation|None|[CBM2022](https://www.sciencedirect.com/science/article/pii/S001048252200751X#!)|
|2022-08|L. Xie and Y. Feng|Semi-supervised region-connectivity-based cerebrovascular segmentation for time-of-flight magnetic resonance angiography image|[Code](https://github.com/IPIS-XieLei/RC-MT)|[CBM2022](https://www.sciencedirect.com/science/article/pii/S0010482522007004#!)|
|2022-08|G. Wang and S. Zhang|PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation|[Code](https://github.com/HiLab-git/PyMIC)|[Arxiv](https://arxiv.org/pdf/2208.09350.pdf)|
|2022-08|J. Zammit and P. Hu|Semi-supervised COVID-19 CT image segmentation using deep generative models|[Code](https://github.com/JudahZammit/stitchnet)|[BMC Bioinformatics](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04878-6)|
|2022-08|Z. Wang and B. Huang|When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentation|[Code](https://github.com/ziyangwang007/CV-SSL-MIS)|[Arxiv2022](https://arxiv.org/pdf/2208.06449.pdf)|
|2022-08|Z. Wang and I. Voiculescu|Triple-View Feature Learning for Medical Image Segmentation|[Code](https://github.com/ziyangwang007/CV-SSL-MIS)|[Arxiv2022](https://arxiv.org/pdf/2208.06303.pdf)|
|2022-08|Z. Zhang and Z. Jiao|Dynamic Prototypical Feature Representation Learning Framework for Semi-supervised Skin Lesion Segmentation|None|[NeuCom2022](https://www.sciencedirect.com/science/article/pii/S092523122201013X?via%3Dihub)|
|2022-08|M. Xu and J. Jacob|Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation|[Code](https://github.com/moucheng2017/EMSSL/)|[MICCAI2022](https://arxiv.org/pdf/2208.04435.pdf)|
|2022-07|X. Li and S. Gao|TCCNet: Temporally Consistent Context-Free Network for Semi-supervised Video Polyp Segmentation|[Code](https://github.com/wener-yung/TCCNet)|[IJCAI2022](https://www.ijcai.org/proceedings/2022/0155.pdf)|
|2022-07|T. Wang and H. Kong|Uncertainty-Guided Pixel Contrastive Learning for Semi-Supervised Medical Image Segmentation|None|[IJCAI2022](https://www.ijcai.org/proceedings/2022/0201.pdf)|
|2022-07|R. Jiao and J. Zhang|Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation|None|[Arxiv2022](https://arxiv.org/pdf/2207.14191.pdf)|
|2022-07|Z. Yang and S. Tang|VoxSeP: semi-positive voxels assist self-supervised 3D medical segmentation|None|[MMSystems2022](https://link.springer.com/article/10.1007/s00530-022-00977-9)|
|2022-07|Z. Xu and T. Lukasiewicz|PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training|None|[Arxiv2022](https://arxiv.org/pdf/2207.11683.pdf)|
|2022-07|N. Shen and J. Li|SCANet: A Unified Semi-supervised Learning Framework for Vessel Segmentation|[Code](https://github.com/shennbit/VESSEL-NIR)|[TMI2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9837087)|
|2022-07|Z. Zhao and C. Guan|MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation|None|[ICIP2022](https://arxiv.org/pdf/2207.01883.pdf)|
|2022-07|Z. Zhao and C. Guan|ACT-Net: Asymmetric Co-Teacher Network for Semi-supervised Memory-efficient Medical Image Segmentation|None|[ArXiv2022](https://arxiv.org/pdf/2207.01900.pdf)
|2022-07|K. Wang and L. Zhou|An Efficient Semi-Supervised Framework with Multi-Task and Curricu-lum Learning for Medical Image Segmentation|[Code](https://github.com/DeepMedLab/MTCL)|[IJNS2022](https://www.worldscientific.com/doi/epdf/10.1142/S0129065722500435)|
|2022-07|B. Fazekas and H. Bogunovi´c|SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors|[Code](https://github.com/ABotond/SD-LayerNet)|[MICCAI2022](https://arxiv.org/pdf/2207.00458.pdf)|
|2022-06|C. Chen and R. Xiao|Generative Consistency for Semi-Supervised Cerebrovascular Segmentation from TOF-MRA|[Code](https://github.com/MontaEllis/SSL-For-Medical-Segmentation)|[TMI2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9801867)|
|2022-06|X. Luo and S. Zhang|Semi-Supervised Medical Image Segmentation via Uncertainty Rectified Pyramid Consistency|[Code](https://github.com/HiLab-git/SSL4MIS)|[MedIA2022](https://www.sciencedirect.com/science/article/pii/S1361841522001645)|
|2022-06|Y. Liu and S. Li|A Contrastive Consistency Semi-supervised Left Atrium Segmentation Model|[Code](https://github.com/PerceptionComputingLab/SCC)|[CMIG2022](https://www.sciencedirect.com/science/article/pii/S0895611122000659?via%3Dihub)|
|2022-06|J. Wang and T. Lukasiewicz|Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation|[Code](https://github.com/Jianf-Wang/GBDL)|[CVPR2022](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Rethinking_Bayesian_Deep_Learning_Methods_for_Semi-Supervised_Volumetric_Medical_Image_CVPR_2022_paper.pdf)|
|2022-06|H. Wu and J. Qin|Cross-patch Dense Contrastive Learning for Semi-supervised Segmentation of Cellular Nuclei in Histopathologic Images|[Code](https://github.com/zzw-szu/CDCL)|[CVPR2022](https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Cross-Patch_Dense_Contrastive_Learning_for_Semi-Supervised_Segmentation_of_Cellular_Nuclei_CVPR_2022_paper.pdf)|
|2022-06|Y. Xiao and G. Yang|Semi-Supervised Segmentation of Mitochondria from Electron Microscopy Images Using Spatial Continuity|[Code](https://github.com/cbmi-group/MPP)|[ISBI2022](https://arxiv.org/ftp/arxiv/papers/2206/2206.02392.pdf)|
|2022-06|X. Liu and J. Woo|ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-Training|None|[MICCAI2022](https://arxiv.org/pdf/2206.02288.pdf)|
|2022-06|C. You and J. Duncan|Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation|None|[Arxiv](https://arxiv.org/pdf/2206.02307.pdf)|
|2022-06|Z. Zhang and Z. Jiao|Mutual- and Self- Prototype Alignment for Semisupervised Medical Image Segmentation|None|[Arxiv](https://arxiv.org/ftp/arxiv/papers/2206/2206.01739.pdf)|
|2022-06|X. Chen and Y. Yu|MASS: Modality-collaborative semi-supervised segmentation by exploiting cross-modal consistency from unpaired CT and MRI images|[Code](https://github.com/xy123chen/MASS)|[MedIA](https://www.sciencedirect.com/science/article/pii/S1361841522001530)|
|2022-05|Y. Lu and M. Meng|Multiple Consistency Supervision based Semi-supervised OCT Segmentation using Very Limited Annotations|None|[ICRA2022](https://ieeexplore.ieee.org/abstract/document/9812447)|
|2022-05|W. Huang and F. Wu|Semi-Supervised Neuron Segmentation via Reinforced Consistency Learning|[Code](https://github.com/weih527/SSNS-Net)|[TMI2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9777694)|
|2022-05|C. Lee and M. Chung|Voxel-wise Adversarial Semi-supervised Learning for Medical Image Segmentation|None|[Arxiv](https://arxiv.org/pdf/2205.06987.pdf)|
|2022-05|Y. Lin and X. Li|Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation|[Code](https://github.com/xmed-lab/CLD-Semi)|[MICCAI2022](https://arxiv.org/pdf/2205.03644.pdf)|
|2022-05|K. Zheng and J. Wei|Double Noise Mean Teacher Self-Ensembling Model for Semi-Supervised Tumor Segmentation|None|[ICASSP2022](https://ieeexplore.ieee.org/abstract/document/9746957)|
|2022-04|Y. Xiao and G. Yang|Semi-Supervised Segmentation of Mitochondria from Electron Microscopy Images Using Spatial Continuity|[Code](https://github.com/cbmi-group/MPP)|[ISBI2022](https://ieeexplore.ieee.org/document/9761519)|
|2022-04|H. He and V. Grau|Semi-Supervised Coronary Vessels Segmentation from Invasive Coronary Angiography with Connectivity-Preserving Loss Function|None|[ISBI2022](https://ieeexplore.ieee.org/document/9761695)|
|2022-04|B. Thompson and J. Voisey|Pseudo-Label Refinement Using Superpixels for Semi-Supervised Brain Tumour Segmentation|None|[ISBI2022](https://ieeexplore.ieee.org/document/9761681)|
|2022-04|Z li and X. Fan|Coupling Deep Deformable Registration with Contextual Refinement for Semi-Supervised Medical Image Segmentation|None|[ISBI2022](https://ieeexplore.ieee.org/document/9761683)|
|2022-04|A. Xu and X. Xia|Ca-Mt: A Self-Ensembling Model for Semi-Supervised Cardiac Segmentation with Elliptical Descriptor Based Contour-Aware|None|[ISBI2022](https://ieeexplore.ieee.org/abstract/document/9761666)|
|2022-04|X. Wang and S. Chen|SSA-Net: Spatial Self-Attention Network for COVID-19 Pneumonia Infection Segmentation with Semi-supervised Few-shot Learning|None|[MedIA2022](https://www.sciencedirect.com/science/article/pii/S1361841522001062)|
|2022-04|Z. Zhang and X. Tian|Discriminative Error Prediction Network for Semi-supervised Colon Gland Segmentation|None|[MedIA2022](https://www.sciencedirect.com/science/article/pii/S1361841522001050)|
|2022-04|Z. Xiao and W. Zhang|Efficient Combination of CNN and Transformer for Dual-Teacher Uncertainty-Aware Guided Semi-Supervised Medical Image Segmentation|None|[SSRN](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4081789)|
|2022-04|K. Han and Z. Liu|An Effective Semi-supervised Approach for Liver CT Image Segmentation|None|[JBHI2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9757875)|
|2022-04|J. Yang and Q. Chen|Self-Supervised Sequence Recovery for SemiSupervised Retinal Layer Segmentation|None|[JBHI2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9756342)|
|2022-04|T. Cheng and C. Cheng|Feature-enhanced Adversarial Semi-supervised Semantic Segmentation Network for Pulmonary Embolism Annotation|None|[Arxiv](https://arxiv.org/ftp/arxiv/papers/2204/2204.04217.pdf)|
|2022-04|K. Wang and Y. Wang|Semi-supervised Medical Image Segmentation via a Tripled-uncertainty Guided Mean Teacher Model with Contrastive Learning|None|[MedIA2022](https://www.sciencedirect.com/science/article/pii/S1361841522000925)|
|2022-04|M. Liu and Q. He|CCAT-NET: A Novel Transformer Based Semi-supervised Framework for Covid-19 Lung Lesion Segmentation|None|[ISBI2022](https://arxiv.org/ftp/arxiv/papers/2204/2204.02839.pdf)|
|2022-03|Y. Liu and G. Carneiro|Translation Consistent Semi-supervised Segmentation for 3D Medical Images|[Code](https://github.com/yyliu01/TraCoCo)|[Arxiv](https://arxiv.org/pdf/2203.14523.pdf)|
|2022-03|Z. Xu and R. Tong|All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation|None|[JBHI2022](https://ieeexplore.ieee.org/document/9741294)|
|2022-03|M. Huang and Q. Feng|Semi-Supervised Hybrid Spine Network for Segmentation of Spine MR Images|[Code](https://github.com/Meiyan88/SSHSNet)|[Arxiv](https://arxiv.org/pdf/2203.12151.pdf)|
|2022-03|S. Adiga V and H. Lombaert|Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation|None|[Arxiv](https://arxiv.org/pdf/2203.05682.pdf)|
|2022-03|M. Tran and T. Peng|S5CL: Unifying Fully-Supervised, Self-Supervised, and Semi-Supervised Learning Through Hierarchical Contrastive Learning|[Code](https://github.com/manuel-tran/s5cl)|[Arxiv](https://arxiv.org/pdf/2203.07307.pdf)|
|2022-03|M. Waerebeke and J. Dole|On the pitfalls of entropy-based uncertainty for multi-class semi-supervised segmentation|None|[Arxiv](https://arxiv.org/pdf/2203.03587.pdf)|
|2022-03|W. Cui and R. M. Leahy|Semi-supervised Learning using Robust Loss|None|[Arxiv](https://arxiv.org/pdf/2203.01524.pdf)|
|2022-02|Z. Fang and Y. Yin|Annotation-Efficient COVID-19 Pneumonia Lesion Segmentation using Error-Aware Unified Semi-supervised and Active Learning|None|[TAI2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9699409)|
|2022-03|Y. Wu and J. Cai|Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation|None|[Arxiv](https://arxiv.org/pdf/2203.01324.pdf)|
|2022-02|Y. Hua and L. Zhang|Uncertainty-Guided Voxel-Level Supervised Contrastive Learning for Semi-Supervised Medical Image Segmentation|None|[IJNS2022](https://www.worldscientific.com/doi/epdf/10.1142/S0129065722500162)|
|2022-02|Y. Shu and W. Li|Cross-Mix Monitoring for Medical Image Segmentation with Limited Supervision|None|[TMM2022](https://ieeexplore.ieee.org/abstract/document/9721091)|
|2022-02|H. Huang and H. Hu|MTL-ABS3Net: Atlas-Based Semi-Supervised Organ Segmentation Network with Multi-Task Learning for Medical Images|None|[JHBI2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9721677)|
|2022-02|H. Wu and J. Qin|Semi-supervised Segmentation of Echocardiography Videos via Noise-resilient Spatiotemporal Semantic Calibration and Fusion|None|[MedIA2022](https://www.sciencedirect.com/science/article/pii/S1361841522000494)|
|2022-02|Z. Liu and C. Zhao|Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Training|None|[Arxiv](https://arxiv.org/ftp/arxiv/papers/2202/2202.06104.pdf)|
|2022-02|X. Zhao and G. Li|Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Segmentation|[Code](https://github.com/ShinkaiZ/CLCC-semi)|[ISBI2022](https://arxiv.org/pdf/2202.04074.pdf)|
|2022-02|H. Basak and A. Chatterjee|An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation|[Code](https://github.com/hritam-98/ICT-MedSeg)|[ISBI2022](https://arxiv.org/abs/2202.00677)|
|2022-01|Q. Chen and D. Ming|Semi-supervised 3D Medical Image Segmentation Based on Dual-task Consistent joint Leanrning and Task-Level Regularization|None|[TCBB2022](https://ieeexplore.ieee.org/document/9689970)|
|2022-01|H. Yao and X. Li|Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation|None|[AAAI2022](https://arxiv.org/pdf/2201.08657.pdf)|H. Basak and A. Chatterjee|An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation|[Code](https://github.com/hritam-98/ICT-MedSeg)|[ISBI2022](https://arxiv.org/abs/2202.00677)|
|2021-12|S. Li and X. Yang|Semi-supervised Cardiac MRI Segmentation Based on Generative Adversarial Network and Variational Auto-Encoder|None|[BIBM2021](https://ieeexplore.ieee.org/document/9669685)|
|2021-12|N. Zhang and Y. Zhang|Semi-supervised Medical Image Segmentation with Distribution Calibration and Non-local Semantic Constraint|None|[BIBM2021](https://ieeexplore.ieee.org/document/9669560)|
|2021-12|S. Liu and G. Cao|Shape-aware Multi-task Learning for Semi-supervised 3D Medical Image Segmentation|None|[BIBM2021](https://ieeexplore.ieee.org/document/9669523)|
|2021-12|X. Xu and P. Yan|Shadow-consistent Semi-supervised Learning for Prostate Ultrasound Segmentation|[Code](https://github.com/DIAL-RPI/SCO-SSL)|[TMI2021](https://ieeexplore.ieee.org/document/9667363)|
|2021-12|L. Hu and Y. Wang|Semi-supervised NPC segmentation with uncertainty and attention guided consistency|None|[KBS2021](https://www.sciencedirect.com/science/article/abs/pii/S0950705121011205)|
|2021-12|J. Peng and M. Pedersoli|Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels|[Code](https://github.com/jizongFox/Self-paced-Contrastive-Learning)|[NeurIPS2021](https://proceedings.neurips.cc/paper/2021/file/8b5c8441a8ff8e151b191c53c1842a38-Paper.pdf)|
|2021-12|Y. Xie and Y. Xia|Intra- and Inter-pair Consistency for Semi-supervised Gland Segmentation|None|[TIP2021](https://ieeexplore.ieee.org/document/9662661)|
2021-12|M. Xu and J. Jacob|Learning Morphological Feature Perturbations for Semi-Supervised Segmentation|[Code](https://github.com/moucheng2017/Morphological_Feature_Perturbation_SSL)|[MIDL2022](https://openreview.net/pdf?id=OL6tAasXCmi)|
|2021-12|X. Luo and S. Zhang|Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer|[Code](https://github.com/HiLab-git/SSL4MIS)|[MIDL2022](https://arxiv.org/pdf/2112.04894.pdf)|
|2021-12|Y. Zhang and J. Zhang|Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation|None|[Arxiv](https://arxiv.org/pdf/2112.02508.pdf)|
|2021-12|J. Wang and Q. Zhou|Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotat|[Code](https://github.com/jcwang123/Separate_CL)|[AAAI2022](https://arxiv.org/pdf/2112.02743.pdf)|
|2021-12|J. Chen and Y. Lu|MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification|[Code](https://github.com/JingyeChen/MT-TransUNet)|[Arxiv](https://arxiv.org/pdf/2112.01767.pdf)|
|2021-12|C. Seibold and R. Stiefelhagen|Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation|None|[AAAI2022](https://arxiv.org/abs/2112.00735)|
|2021-11|X. Zheng and C. Sham|Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation|None|[Arxiv](https://arxiv.org/pdf/2111.11629v1.pdf)|
|2021-11|J. Peng and M. Pedersoli|Diversified Multi-prototype Representation for Semi-supervised Segmentation|[Code](https://github.com/jizongFox/MI-based-Regularized-Semi-supervised-Segmentation)|[Arxiv](https://arxiv.org/pdf/2111.08651.pdf)|
|2021-10|J. Hou and J. Deng|Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations|None|[WACV2021](https://arxiv.org/pdf/2110.11998.pdf)|
|2021-10|M. Xu and J. Jacob|MisMatch: Learning to Change Predictive Confidences with Attention for Consistency-Based, Semi-Supervised Medical Image Segmentation|None|[Arxiv](https://arxiv.org/pdf/2110.12179.pdf)|
|2021-10|H. Wu and J. Qin|Collaborative and Adversarial Learning of Focused and Dispersive Representations for Semi-supervised Polyp Segmentation|None|[ICCV2021](https://openaccess.thecvf.com/content/ICCV2021/papers/Wu_Collaborative_and_Adversarial_Learning_of_Focused_and_Dispersive_Representations_for_ICCV_2021_paper.pdf)|
|2021-10|Y. Shi and Y. Gao|Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation|[Code](https://github.com/koncle/CoraNet)|[TMI2021](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9558816)|
|2021-09|G. Wang and S. Zhang|Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis from Lung CT Scans with Multi-Scale Guided Dense Attention|[Code](https://github.com/HiLab-git/PF-Net)|[TMI2021](https://arxiv.org/pdf/2109.14172.pdf)|
|2021-09|K. Wang and Y. Wang|Tripled-Uncertainty Guided Mean Teacher Model for Semi-supervised Medical Image Segmentation|[Code](https://github.com/DeepMedLab/Tri-U-MT)|[MICCAI2021](https://link.springer.com/chapter/10.1007/978-3-030-87196-3_42)|
|2021-09|H. Huang and R. Tong|3D Graph-S2Net: Shape-Aware Self-ensembling Network for Semi-supervised Segmentation with Bilateral Graph Convolution|None|[MICCAI2021](https://link.springer.com/chapter/10.1007/978-3-030-87196-3_39)|
|2021-09|L. Zhu and B. Ooi|Semi-Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation|[Code](https://github.com/nusdbsystem/SSUMML)|[MICCAI2021](https://link.springer.com/chapter/10.1007/978-3-030-87196-3_37)|
|2021-09|R. Zhang and G. Li|Self-supervised Correction Learning for Semi-supervised Biomedical Image Segmentation|[Code](https://github.com/ReaFly/SemiMedSeg)|[MICCAI2021](https://link.springer.com/chapter/10.1007/978-3-030-87196-3_13)|
|2021-09|D. Kiyasseh and A. Chen|Segmentation of Left Atrial MR Images via Self-supervised Semi-supervised Meta-learning|None|[MICCAI2021](https://link.springer.com/chapter/10.1007/978-3-030-87196-3_2)|
|2021-09|Y. Wu and J. Cai|Enforcing Mutual Consistency of Hard Regions for Semi-supervised Medical Image Segmentation|None|[Arxiv](https://arxiv.org/pdf/2109.09960.pdf)|
|2021-09|X. Zeng and Y. Wang|Reciprocal Learning for Semi-supervised Segmentation|[Code](https://github.com/XYZach/RLSSS)|[MICCAI2021](https://dilincv.github.io/papers/reciprocal_miccai2021.pdf)|
|2021-09|G. Zhang and S. Jiang|Automatic segmentation of organs at risk and tumors in CT images of lung cancer from partially labelled datasets with a semi-supervised conditional nnU-Net|None|[CMPB2021](https://doi.org/10.1016/j.cmpb.2021.106419)|
|2021-09|J. Chen and G. Yang|Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data|[Code](https://github.com/Heye-SYSU/AHDC)|[TMI2021](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9540830)|
|2021-09|X. Hu and Y. Shi|Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation|[Code](https://github.com/xhu248/semi_cotrast_seg)|[MICCAI2021](https://arxiv.org/pdf/2109.07407.pdf)|
|2021-09|G. Chen and J. Shi|MTANS: Multi-Scale Mean Teacher Combined Adversarial Network with Shape-Aware Embedding for Semi-Supervised Brain Lesion Segmentation|[Code](https://github.com/wzcgx/MTANS)|[NeuroImage2021](https://www.sciencedirect.com/science/article/pii/S1053811921008417)|
|2021-08|H. Peiris and M. Harandi|Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation|[Code](https://github.com/himashi92/Duo-SegNet)|[MICCAI2021](https://arxiv.org/pdf/2108.11154.pdf)|
|2021-08|J. Sun and Y. Kong|Semi-Supervised Medical Image Semantic Segmentation with Multi-scale Graph Cut Loss|None|[ICIP2021](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9506098)|
|2021-08|X. Shen and J. Lu|PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning|None|[ArXiv](https://arxiv.org/pdf/2108.11694.pdf)|
|2021-08|C. You and J. Duncan|SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation|None|[Arxiv](https://arxiv.org/pdf/2108.06227.pdf)|
|2021-08|C. Li and P. Heng|Self-Ensembling Co-Training Framework for Semi-supervised COVID-19 CT Segmentation|None|[JBHI2021](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9511146)|
|2021-08|H. Yang and P. H. N. With|Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning|None|[JBHI2021](https://arxiv.org/pdf/2107.14476.pdf)|
|2021-07|Q. Xu and X. Wang|Semi-supervised Medical Image Segmentation with Confidence Calibration|None|[IJCNN](https://ieeexplore.ieee.org/document/9534435)|
|2021-07|W. Ding and H. Hawash|RCTE: A Reliable and Consistent Temporal-ensembling Framework for Semi-supervised Segmentation of COVID-19 Lesions|None|[Information Fusion2021](https://www.sciencedirect.com/science/article/pii/S0020025521007490)|
|2021-06|X. Liu and S. A. Tsaftaris|Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation|[Code](https://github.com/vios-s/DGNet)|[MICCAI2021](https://arxiv.org/pdf/2106.13292.pdf)|
|2021-06|P. Pandey and Mausam|Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation|None|[MICCAI2021](https://arxiv.org/pdf/2106.06801v1.pdf)|
|2021-06|C. Li and Y. Yu|Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation|None|[Arxiv](https://arxiv.org/pdf/2105.14732.pdf)|
|2021-05|J. Xiang and S. Zhang|Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation|None|[Arxiv](https://arxiv.org/pdf/2105.12924.pdf)|
|2021-05|S. Li and C. Guan|Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation|None|[Arxiv](https://arxiv.org/pdf/2105.10369.pdf)|
|2021-05|C. You and J. Duncan|Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation|None|[MICCAI2022](https://arxiv.org/pdf/2105.07059.pdf)|
|2021-05|Z. Xie and J. Yang|Semi-Supervised Skin Lesion Segmentation with Learning Model Confidence|None|[ICASSP2021](https://ieeexplore.ieee.org/document/9414297)|
|2021-04|S. Reiß and R. Stiefelhagen|Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation|None|[CVPR2021](https://arxiv.org/pdf/2104.13243.pdf)|
|2021-04|S. Chatterjee and A. Nurnberger|DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data|[Code](https://github.com/soumickmj/DS6)|[MIDL](https://openreview.net/pdf?id=2t0_AxD1otB)|
|2021-04|A. Meyer and M. Rak|Uncertainty-Aware Temporal Self-Learning (UATS): Semi-Supervised Learning for Segmentation of Prostate Zones and Beyond|[Code](https://github.com/suhitaghosh10/UATS)|[Arxiv](https://arxiv.org/pdf/2104.03840.pdf)|
|2021-04|Y. Li and P. Heng|Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images|None|[MICCAI2021](https://arxiv.org/pdf/2104.03225.pdf)|
|2021-03|Y. Zhang and C. Zhang|Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation|[Code](https://github.com/YichiZhang98/DTML)|[PRCV2021](https://arxiv.org/pdf/2103.04708.pdf)|
|2021-03|J. Peng and C. Desrosiers|Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization|[Code](https://github.com/jizongFox/MI-based-Regularized-Semi-supervised-Segmentation)|[MELBA](https://arxiv.org/pdf/2103.04813.pdf)|
|2021-03|Y. Wu and L. Zhang|Semi-supervised Left Atrium Segmentation with Mutual Consistency Training|None|[MICCAI2021](https://arxiv.org/pdf/2103.02911)|
|2021-02|J. Peng and Y. Wang|Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models|None|[Arxiv](https://arxiv.org/pdf/2103.00429.pdf)|
|2021-02|J. Dolz and I. B. Ayed|Teach me to segment with mixed supervision: Confident students become masters|[Code](https://github.com/josedolz/MSL-student-becomes-master)|[IPMI2021](https://arxiv.org/pdf/2012.08051.pdf)|
|2021-02|C. Cabrera and K. McGuinness|Semi-supervised Segmentation of Cardiac MRI using Image Registration|None|[Under review for MIDL2021](https://openreview.net/pdf?id=ZMBea7SLdi)|
|2021-02|Y. Wang and A. Yuille|Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction|None|[TMI2021](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9357342)|
|2021-02|R. Alizadehsaniand U R. Acharya|Uncertainty-Aware Semi-supervised Method using Large Unlabelled and Limited Labeled COVID-19 Data|None|[Arxiv](https://arxiv.org/ftp/arxiv/papers/2102/2102.06388.pdf)|
|2021-02|D. Yang and D. Xu|Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan|None|[MedIA2021](https://www.sciencedirect.com/science/article/pii/S1361841521000384)|
|2020-01|E. Takaya and S. Kurihara|Sequential Semi-supervised Segmentation for Serial Electron Microscopy Image with Small Number of Labels|[Code](https://github.com/eichitakaya/Sequential-Semi-supervised-Segmentation)|[Journal of Neuroscience Methods](https://www.sciencedirect.com/science/article/pii/S0165027021000017)|
|2021-01|Y. Zhang and Z. He|Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer|None|[Arxiv](https://arxiv.org/pdf/2012.14785.pdf)|
|2020-12|H. Wang and D. Chen|Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation|None|[Arxiv](https://arxiv.org/pdf/2012.09373.pdf)|
|2020-12|X. Luo and S. Zhang|Efficient Semi-supervised  Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency|[Code](https://github.com/HiLab-git/SSL4MIS)|[MICCAI2021](https://arxiv.org/pdf/2012.07042.pdf)|
|2020-12|M. Abdel‐Basset and M. Ryan|FSS-2019-nCov: A Deep Learning Architecture for Semi-supervised Few-Shot Segmentation of COVID-19 Infection|None|[Knowledge-Based Systems2020](https://www.sciencedirect.com/science/article/pii/S0950705120307760)|
|2020-11|A. Chartsias and S. A. Tsaftaris|Disentangle, Align and Fuse for Multimodal and Semi-Supervised Image Segmentation|[Code](https://github.com/vios-s/multimodal_segmentation)|[TMI2021](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9250615)|
|2020-11|N. Horlava and N. Scherf|A comparative study of semi- and self-supervised semantic segmentation of biomedical microscopy data|None|[Arxiv](https://arxiv.org/pdf/2011.08076.pdf)|
|2020-11|P. Wang and C. Desrosiers|Self-paced and self-consistent co-training for semi-supervised image segmentation|None|[MedIA2021](https://arxiv.org/pdf/2011.00325.pdf)|
|2020-10|Y. Sun and L. Wang|Semi-supervised Transfer Learning for Infant Cerebellum Tissue Segmentation|None|[MLMI2020](http://liwang.web.unc.edu/files/2020/10/Sun2020_Chapter_Semi-supervisedTransferLearnin.pdf)|
|2020-10|L. Chen and D. Merhof|Semi-supervised Instance Segmentation with a Learned Shape Prior|[Code](https://github.com/looooongChen/shape_prior_seg)|[LABELS2020](https://link.springer.com/chapter/10.1007/978-3-030-61166-8_10)|
|2020-10|S. Shailja and B.S. Manjunath|Semi supervised segmentation and graph-based tracking of 3D nuclei in time-lapse microscopy|[Code](https://github.com/s-shailja/ucsb_ctc)|[Arxiv](https://arxiv.org/pdf/2010.13343.pdf)|
|2020-10|L. Sun and Y. Yu|A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision|None|[Arxiv](https://arxiv.org/pdf/2010.12219.pdf)|
|2020-10|J. Ma and X. Yang|Active Contour Regularized Semi-supervised Learning for COVID-19 CT Infection Segmentation with Limited Annotations|[Code](https://zenodo.org/record/4246238#.X6PSyogzZFE)|[Physics in Medicine & Biology2020](https://iopscience.iop.org/article/10.1088/1361-6560/abc04e/pdf)|
|2020-10|W. Hang and J. Qin|Local and Global Structure-Aware Entropy Regularized Mean Teacher Model for 3D Left Atrium Segmentation|[Code](https://github.com/3DMRIs/LG-ER-MT)|[MICCAI2020](https://link.springer.com/chapter/10.1007/978-3-030-59710-8_55)|
|2020-10|K. Tan and J. Duncan|A Semi-supervised Joint Network for Simultaneous Left Ventricular Motion Tracking and Segmentation in 4D Echocardiography|None|[MICCAI2020](https://link.springer.com/chapter/10.1007/978-3-030-59725-2_45)|
|2020-10|Y. Wang and Z. He|Double-Uncertainty Weighted Method for Semi-supervised Learning|None|[MICCAI2020](https://link.springer.com/chapter/10.1007%2F978-3-030-59710-8_53)|
|2020-10|K. Fang and W. Li|DMNet: Difference Minimization Network for Semi-supervised Segmentation in Medical Images|None|[MICCAI2020](https://link.springer.com/chapter/10.1007/978-3-030-59710-8_52)|
|2020-10|X. Cao and L. Cheng|Uncertainty Aware Temporal-Ensembling Model for Semi-supervised ABUS Mass Segmentation|None|[TMI2020](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9214845)|
|2020-09|Z. Zhang and W. Zhang|Semi-supervised Semantic Segmentation of Organs at Risk on 3D Pelvic CT Images|None|[Arxiv](https://arxiv.org/ftp/arxiv/papers/2009/2009.09571.pdf)|
|2020-09|J. Wang and G. Xie|Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions|None|[BMVC2020](http://scholar.google.com/scholar_url?url=https://www.bmvc2020-conference.com/assets/papers/0031.pdf&hl=zh-CN&sa=X&d=4465129548770333798&ei=u85pX6XsJNKsmwG4zr6YCw&scisig=AAGBfm1GGUNfq7zId6WBRyppRRjnPSpLsQ&nossl=1&oi=scholaralrt&html=&cited-by=)|
|2020-09|X. Luo and S. Zhang|Semi-supervised Medical Image Segmentation through Dual-task Consistency|[Code](https://github.com/Luoxd1996/DTC)|[AAAI2021](https://arxiv.org/pdf/2009.04448.pdf)|
|2020-08|X. Huo and Q. Tian|ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation|None|[Arxiv](https://arxiv.org/pdf/2006.13461.pdf)|
|2020-08|Y. Xie and Y. Xia|Pairwise Relation Learning for Semi-supervised Gland Segmentation|None|[MICCAI2020](https://arxiv.org/pdf/2008.02699.pdf)|
|2020-07|K. Chaitanya and E. Konukoglu|Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation|[Code](https://github.com/krishnabits001/task_driven_data_augmentation)|[Arxiv](https://arxiv.org/pdf/2007.05363.pdf)|
|2020-07|H. Ni and X. Huang|SiamParseNet: Joint Body Parsing and Label Propagation in Infant Movement Videos|[Code](https://github.com/nihaomiao/MICCAI20_SiamParseNet)|[MICCAI2020](https://arxiv.org/abs/2007.08646)|
|2020-07|S. Li and X. He|Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images|[Code](https://github.com/kleinzcy/SASSnet)|[MICCAI2020](https://arxiv.org/pdf/2007.10732.pdf)|
|2020-07|Y. Li and Y. Zheng |Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation|None|[MICCAI2020](https://arxiv.org/abs/2007.09854)|
|2020-07|Z. Zhao and P. Heng|Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video|[Code](https://github.com/zxzhaoeric/Semi-InstruSeg/)|[MICCAI2020](https://arxiv.org/abs/2007.02501)|
|2020-07|Y. Zhou and P. Heng|Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation|[Code](https://github.com/SIAAAAAA/MMT-PSM)|[MICCAI2020](https://arxiv.org/pdf/2007.10787.pdf)|
|2020-07|A. Tehrani and H. Rivaz|Semi-Supervised Training of Optical Flow Convolutional Neural Networks in Ultrasound Elastography|None|[MICCAI2020](https://arxiv.org/pdf/2007.01421.pdf)|
|2020-07|Y. He and S. Li|Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised learning for fine renal artery segmentation|None|[MedIA2020](https://www.sciencedirect.com/science/article/pii/S1361841520300864)|
|2020-07|J. Peng and C. Desrosiers|Mutual information deep regularization for semi-supervised segmentation|[Code](https://github.com/jizongFox/deep-clustering-toolbox)|[MIDL2020](https://openreview.net/pdf?id=iunvffXgPm)|
|2020-07|Y. Xia and H. Roth|Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation|None|[WACV2020](https://arxiv.org/abs/1811.12506),[MedIA2020](https://www.sciencedirect.com/science/article/pii/S1361841520301304)|
|2020-07|X. Li and P. Heng|Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation|[Code](https://github.com/xmengli999/TCSM)|[TNNLS2020](https://ieeexplore.ieee.org/document/9104928)|
|2020-06|F. Garcıa and S. Ourselin|Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning|None|[MICCAI2020](https://arxiv.org/pdf/2006.15693.pdf)|
|2020-06|H. Yang and P. With|Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet|None|[MICCAI2020](https://arxiv.org/pdf/2006.14702.pdf)|
|2020-05|G. Fotedar and X. Ding|Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts|None|[MICCAI2020](https://link.springer.com/chapter/10.1007/978-3-030-59710-8_68)|
|2020-04|C. Liu and C. Ye|Semi-Supervised Brain Lesion Segmentation Using Training Images with and Without Lesions|None|[ISBI2020](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9098565)|
|2020-04|R. Li and D. Auer|A Generic Ensemble Based Deep Convolutional Neural Network for Semi-Supervised Medical Image Segmentation|[Code](https://github.com/ruizhe-l/semi-segmentation)|[ISBI2020](https://arxiv.org/pdf/2004.07995.pdf)|
|2020-04|K. Ta and J. Duncan|A Semi-Supervised Joint Learning Approach to Left Ventricular Segmentation and Motion Tracking in Echocardiography|None|[ISBI2020](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9098664)|
|2020-04|Q. Chang and D. Metaxas|Soft-Label Guided Semi-Supervised Learning for Bi-Ventricle Segmentation in Cardiac Cine MRI|None|[ISBI2020](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9098546)|
|2020-04|D. Fan and L. Shao|Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images|[Code](https://github.com/DengPingFan/Inf-Net)|[TMI2020](https://ieeexplore.ieee.org/document/9098956)|
|2019-10|L. Yu and P. Heng|Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation|[Code](https://github.com/yulequan/UA-MT)|[MICCAI2019](https://arxiv.org/pdf/1907.07034.pdf)|
|2019-10|G. Bortsova and M. Bruijne|Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations|None|[MICCAI2019](https://arxiv.org/pdf/1911.01218.pdf)|
|2019-10|Y. He and S. Li|DPA-DenseBiasNet: Semi-supervised 3D Fine Renal Artery Segmentation with Dense Biased Network and Deep Priori Anatomy|None|[MICCAI2019](https://link.springer.com/chapter/10.1007/978-3-030-32226-7_16)|
|2019-10|H. Zheng and X. Han|Semi-supervised Segmentation of Liver Using Adversarial Learning with Deep Atlas Prior|None|[MICCAI2019](https://link.springer.com/chapter/10.1007/978-3-030-32226-7_17)|
|2019-10|P. Ganayea and H. Cattin|Removing Segmentation Inconsistencies with Semi-Supervised Non-Adjacency Constraint|[Code](https://github.com/trypag/NonAdjLoss)|[MedIA2019](https://www.sciencedirect.com/science/article/abs/pii/S1361841519300866)|
|2019-10|Y. Zhao and C. Liu|Multi-view Semi-supervised 3D Whole Brain Segmentation with a Self-ensemble Network|None|[MICCAI2019](https://link.springer.com/chapter/10.1007/978-3-030-32248-9_29)|
|2019-10|H. Kervade and I. Ayed|Curriculum semi-supervised segmentation|None|[MICCAI2019](https://arxiv.org/pdf/1904.05236.pdf)|
|2019-10|S. Chen and M. Bruijne|Multi-task Attention-based Semi-supervised Learning for Medical Image Segmentation|None|[MICCAI2019](https://arxiv.org/pdf/1907.12303.pdf)|
|2019-10|Z. Xu and M. Niethammer|DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation|None|[MICCAI2019](https://arxiv.org/pdf/1904.08465.pdf)|
|2019-10|S. Sedai and R. Garnavi|Uncertainty Guided Semi-supervised Segmentation of Retinal Layers in OCT Images|None|[MICCAI2019](https://link.springer.com/chapter/10.1007/978-3-030-32239-7_32)|
|2019-10|G. Pombo and P. Nachev|Bayesian Volumetric Autoregressive Generative Models for Better Semisupervised Learning|[Code](https://github.com/guilherme-pombo/3DPixelCNN)|[MICCAI2019](https://link.springer.com/chapter/10.1007/978-3-030-32251-9_47)|
|2019-06|W. Cui and C. Ye|Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model|None|[IPMI2019](https://link.springer.com/chapter/10.1007/978-3-030-20351-1_43)|
|2019-06|K. Chaitanya and E. Konukoglu|Semi-supervised and Task-Driven Data Augmentation|[Code](https://github.com/krishnabits001/task_driven_data_augmentation)|[IPMI2019](http://link-springer-com-443.webvpn.fjmu.edu.cn/chapter/10.1007%2F978-3-030-20351-1_3)|
|2019-04|M. Jafari and P. Abolmaesumi|Semi-Supervised Learning For Cardiac Left Ventricle Segmentation Using Conditional Deep Generative Models as Prior|None|[ISBI2019](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8759292)|
|2019-03|Z. Zhao and Z. Zeng|Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation|None|[BHI](https://ieeexplore.ieee.org/document/8834460)|
|2019-03|J. Peng and C. Desrosiers|Deep co-training for semi-supervised image segmentation|[Code](https://github.com/jizongFox/deep-clustering-toolbox)|[PR2020](https://www.sciencedirect.com/science/article/pii/S0031320320300741/pdfft?md5=ecbfff8052e991b23c1796f97588d7e5&pid=1-s2.0-S0031320320300741-main.pdf)|
|2019-01|Y. Zhou and A. Yuille|Semi-Supervised 3D Abdominal Multi-Organ Segmentation via Deep Multi-Planar Co-Training|None|[WACV2019](http://www.robots.ox.ac.uk/~tvg/publications/2019/dmpct_wacv.pdf)|
|2018-10|P. Ganaye and H. Cattin|Semi-supervised Learning for Segmentation Under Semantic Constraint|[Code](https://github.com/trypag/NonAdjLoss)|[MICCAI2018](https://link.springer.com/chapter/10.1007/978-3-030-00931-1_68)|
|2018-10|A. Chartsias and S. Tsaftari|Factorised spatial representation learning: application in semi-supervised myocardial segmentation|None|[MICCAI2018](https://arxiv.org/pdf/1803.07031.pdf)|
|2018-09|X. Li and P. Heng|Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model|[Code](https://github.com/xmengli999/TCSM)|[BMVC2018](https://arxiv.org/pdf/1808.03887.pdf)|
|2018-04|Z. Feng and D. Shen|Semi-supervised learning for pelvic MR image segmentation based on multi-task residual fully convolutional networks|None|[ISBI2018](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8363713)|
|2017-09|L. Gu and S. Aiso|Semi-supervised Learning for Biomedical Image Segmentation via Forest Oriented Super Pixels(Voxels)|None|[MICCAI2017](https://link.springer.com/chapter/10.1007/978-3-319-66182-7_80)|
|2017-09|S. Sedai and R. Garnavi|Semi-supervised Segmentation of Optic Cup in Retinal Fundus Images Using Variational Autoencoder|None|[MICCAI2017](https://link.springer.com/chapter/10.1007/978-3-319-66185-8_9)|
|2017-09|W. Bai and D. Rueckert|Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation|None|[MICCAI2017](https://link.springer.com/chapter/10.1007/978-3-319-66185-8_29)|
|2016-09|D. Mahapatra|Semi-supervised learning and graph cuts for consensus based medical image segmentation|None|[PR2016](https://www.sciencedirect.com/science/article/pii/S0031320316302904)|

## Code for semi-supervised medical image segmentation.
Some implementations of semi-supervised learning methods can be found in this [Link](https://github.com/Luoxd1996/SSL4MIS/tree/master/code).

## Conclusion
* This repository provides daily-update literature reviews, algorithms' implementation, and some examples of using PyTorch for semi-supervised medical image segmentation. The project is under development. Currently, it supports 2D and 3D semi-supervised image segmentation and includes five widely-used algorithms' implementations.
		
* In the next two or three months, we will provide more algorithms' implementations, examples, and pre-trained models.

## Questions and Suggestions
* If you have any questions or suggestions about this project, please contact me through email: `luoxd1996@gmail.com` or QQ Group (Chinese):`906808850`.