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[New], We are reformatting the codebase to support the 5-fold cross-validation and randomly select labeled cases, the reformatted methods in this Branch.
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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.
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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.
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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}}
| Date | The First and Last Authors | Title | Code | Reference |
|---|---|---|---|---|
| 2023-03 | Y. Zhu and R. Zhang | Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation | Code | MIDL2023 |
| 2023-03 | C. Xu and S. Zhao | Dual Uncertainty-guided Mixing Consistency for Semi-Supervised 3D Medical Image Segmentation | Code | TBD2023 |
| 2023-03 | K. Chaitanya and E. Konukoglu | Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation | Code | MedIA2023 |
| 2023-03 | J. Zhu and E. Meijering | Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans | Code | Arxiv |
| 2023-02 | P. Wang and C. Desrosiers | CAT: Constrained Adversarial Training for Anatomically-plausible Semi-supervised Segmentation | Code | TMI2023 |
| 2023-02 | C. You and J. Duncan | Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective | None | Arxiv |
| 2023-02 | L. Zeng and W. Wang | SS-TBN: A Semi-Supervised Tri-Branch Network for COVID-19 Screening and Lesion Segmentation | None | TPAMI2023 |
| 2023-01 | X. Zhao and L. Zhang | RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation | Code | Arxiv |
| 2023-01 | Z. Shen and O. Zaiane | Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation | Code | Arxiv |
| 2023-01 | D. Chen and Y. Wang | MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery | None | Arxiv |
| 2022-12 | P. Qiao and J. Chen | Semi-supervised CT Lesion Segmentation Using Uncertainty-based Data Pairing and SwapMix | None | TMI2022 |
| 2022-12 | Z. Wang and Z. Ni | Adversarial Vision Transformer for Medical Image Semantic Segmentation with Limited Annotations | Code | BMVC2022 |
| 2022-12 | T. Lei and A. Nandi | Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network | Code | TMI2022 |
| 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 | TCYB2022 |
| 2022-10 | F. Fyu and P. Yuen | Pseudo-Label Guided Image Synthesis for Semi-Supervised COVID-19 Pneumonia Infection Segmentation | Code | TMI2022 |
| 2022-10 | J. Shi and C. Li | Semi-Supervised Pixel Contrastive Learning Framework for Tissue Segmentation in Histopathological Image | None | JBHI2022 |
| 2022-10 | C. Xu and S. Li | BMAnet: Boundary Mining with Adversarial Learning for Semi-supervised 2D Myocardial Infarction Segmentation | None | JBHI2022 |
| 2022-10 | D. Xiang and B. Tian | Semi-supervised Dual Stream Segmentation Network for Fundus Lesion Segmentation | None | TMI2022 |
| 2022-10 | F. Wu and X. Zhuang | Minimizing Estimated Risks on Unlabeled Data: A New Formulation for Semi-Supervised Medical Image Segmentation | Code | TPAMI2022 |
| 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 |
| 2022-10 | J. Chen and J. Han | Semi-supervised Unpaired Medical Image Segmentation Through Task-affinity Consistency | Code | TMI2022 |
| 2022-09 | H. Huang and Y. Zou | Complementary consistency semi-supervised learning for 3D left atrial image segmentation | Code | Arxiv |
| 2022-09 | R. Gu and S. Zhang | Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures | Code | TMI2022 |
| 2022-09 | Q. Jin and R. Su | Semi-supervised Histological Image Segmentation via Hierarchical Consistency Enforcement | None | MICCAI2022 |
| 2022-09 | J. Xiang and Y. Yang | FUSSNet: Fusing Two Sources of Uncertainty for Semi-supervised Medical Image Segmentation | Code | MICCAI2022 |
| 2022-09 | V. Nath and D. Xu | Warm Start Active Learning with Proxy Labels and Selection via Semi-supervised Fine-Tuning | None | MICCAI2022 |
| 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 | MICCAI2022 |
| 2022-09 | Y. Meng and Y. Zheng | Shape-Aware Weakly/Semi-Supervised Optic Disc and Cup Segmentation with Regional/Marginal Consistency | Code | MICCAI2022 |
| 2022-09 | X. Zhao and G. Li | Semi-supervised Spatial Temporal Attention Network for Video Polyp Segmentation | Code | MICCAI2022 |
| 2022-09 | J. Wu and D. Ding | Semi-supervised Learning for Nerve Segmentation in Corneal Confocal Microscope Photography | None | MICCAI2022 |
| 2022-09 | H. Basak and R. Sarkar | Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI | None | MICCAI2022 |
| 2022-08 | Q. Wang and J. Chen | A regularization-driven Mean Teacher model based on semi-supervised learning for medical image segmentation | Code | PMB2022 |
| 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 | TMI2022 |
| 2022-08 | T. Lei and B. Lu | Semi-Supervised 3D Medical Image Segmentation Using Shape-Guided Dual Consistency Learning | None | ICME2022 |
| 2022-08 | J. Chen and C. Sham | Uncertainty teacher with dense focal loss for semi-supervised medical image segmentation | None | CBM2022 |
| 2022-08 | L. Xie and Y. Feng | Semi-supervised region-connectivity-based cerebrovascular segmentation for time-of-flight magnetic resonance angiography image | Code | CBM2022 |
| 2022-08 | G. Wang and S. Zhang | PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation | Code | Arxiv |
| 2022-08 | J. Zammit and P. Hu | Semi-supervised COVID-19 CT image segmentation using deep generative models | Code | BMC Bioinformatics |
| 2022-08 | Z. Wang and B. Huang | When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentation | Code | Arxiv2022 |
| 2022-08 | Z. Wang and I. Voiculescu | Triple-View Feature Learning for Medical Image Segmentation | Code | Arxiv2022 |
| 2022-08 | Z. Zhang and Z. Jiao | Dynamic Prototypical Feature Representation Learning Framework for Semi-supervised Skin Lesion Segmentation | None | NeuCom2022 |
| 2022-08 | M. Xu and J. Jacob | Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation | Code | MICCAI2022 |
| 2022-07 | X. Li and S. Gao | TCCNet: Temporally Consistent Context-Free Network for Semi-supervised Video Polyp Segmentation | Code | IJCAI2022 |
| 2022-07 | T. Wang and H. Kong | Uncertainty-Guided Pixel Contrastive Learning for Semi-Supervised Medical Image Segmentation | None | IJCAI2022 |
| 2022-07 | R. Jiao and J. Zhang | Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation | None | Arxiv2022 |
| 2022-07 | Z. Yang and S. Tang | VoxSeP: semi-positive voxels assist self-supervised 3D medical segmentation | None | MMSystems2022 |
| 2022-07 | Z. Xu and T. Lukasiewicz | PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training | None | Arxiv2022 |
| 2022-07 | N. Shen and J. Li | SCANet: A Unified Semi-supervised Learning Framework for Vessel Segmentation | Code | TMI2022 |
| 2022-07 | Z. Zhao and C. Guan | MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation | None | ICIP2022 |
| 2022-07 | Z. Zhao and C. Guan | ACT-Net: Asymmetric Co-Teacher Network for Semi-supervised Memory-efficient Medical Image Segmentation | None | ArXiv2022 |
| 2022-07 | K. Wang and L. Zhou | An Efficient Semi-Supervised Framework with Multi-Task and Curricu-lum Learning for Medical Image Segmentation | Code | IJNS2022 |
| 2022-07 | B. Fazekas and H. Bogunovi´c | SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors | Code | MICCAI2022 |
| 2022-06 | C. Chen and R. Xiao | Generative Consistency for Semi-Supervised Cerebrovascular Segmentation from TOF-MRA | Code | TMI2022 |
| 2022-06 | X. Luo and S. Zhang | Semi-Supervised Medical Image Segmentation via Uncertainty Rectified Pyramid Consistency | Code | MedIA2022 |
| 2022-06 | Y. Liu and S. Li | A Contrastive Consistency Semi-supervised Left Atrium Segmentation Model | Code | CMIG2022 |
| 2022-06 | J. Wang and T. Lukasiewicz | Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation | Code | CVPR2022 |
| 2022-06 | H. Wu and J. Qin | Cross-patch Dense Contrastive Learning for Semi-supervised Segmentation of Cellular Nuclei in Histopathologic Images | Code | CVPR2022 |
| 2022-06 | Y. Xiao and G. Yang | Semi-Supervised Segmentation of Mitochondria from Electron Microscopy Images Using Spatial Continuity | Code | ISBI2022 |
| 2022-06 | X. Liu and J. Woo | ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-Training | None | MICCAI2022 |
| 2022-06 | C. You and J. Duncan | Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation | None | Arxiv |
| 2022-06 | Z. Zhang and Z. Jiao | Mutual- and Self- Prototype Alignment for Semisupervised Medical Image Segmentation | None | Arxiv |
| 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 | MedIA |
| 2022-05 | Y. Lu and M. Meng | Multiple Consistency Supervision based Semi-supervised OCT Segmentation using Very Limited Annotations | None | ICRA2022 |
| 2022-05 | W. Huang and F. Wu | Semi-Supervised Neuron Segmentation via Reinforced Consistency Learning | Code | TMI2022 |
| 2022-05 | C. Lee and M. Chung | Voxel-wise Adversarial Semi-supervised Learning for Medical Image Segmentation | None | Arxiv |
| 2022-05 | Y. Lin and X. Li | Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation | Code | MICCAI2022 |
| 2022-05 | K. Zheng and J. Wei | Double Noise Mean Teacher Self-Ensembling Model for Semi-Supervised Tumor Segmentation | None | ICASSP2022 |
| 2022-04 | Y. Xiao and G. Yang | Semi-Supervised Segmentation of Mitochondria from Electron Microscopy Images Using Spatial Continuity | Code | ISBI2022 |
| 2022-04 | H. He and V. Grau | Semi-Supervised Coronary Vessels Segmentation from Invasive Coronary Angiography with Connectivity-Preserving Loss Function | None | ISBI2022 |
| 2022-04 | B. Thompson and J. Voisey | Pseudo-Label Refinement Using Superpixels for Semi-Supervised Brain Tumour Segmentation | None | ISBI2022 |
| 2022-04 | Z li and X. Fan | Coupling Deep Deformable Registration with Contextual Refinement for Semi-Supervised Medical Image Segmentation | None | ISBI2022 |
| 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 |
| 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 |
| 2022-04 | Z. Zhang and X. Tian | Discriminative Error Prediction Network for Semi-supervised Colon Gland Segmentation | None | MedIA2022 |
| 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 |
| 2022-04 | K. Han and Z. Liu | An Effective Semi-supervised Approach for Liver CT Image Segmentation | None | JBHI2022 |
| 2022-04 | J. Yang and Q. Chen | Self-Supervised Sequence Recovery for SemiSupervised Retinal Layer Segmentation | None | JBHI2022 |
| 2022-04 | T. Cheng and C. Cheng | Feature-enhanced Adversarial Semi-supervised Semantic Segmentation Network for Pulmonary Embolism Annotation | None | Arxiv |
| 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 |
| 2022-04 | M. Liu and Q. He | CCAT-NET: A Novel Transformer Based Semi-supervised Framework for Covid-19 Lung Lesion Segmentation | None | ISBI2022 |
| 2022-03 | Y. Liu and G. Carneiro | Translation Consistent Semi-supervised Segmentation for 3D Medical Images | Code | Arxiv |
| 2022-03 | Z. Xu and R. Tong | All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation | None | JBHI2022 |
| 2022-03 | M. Huang and Q. Feng | Semi-Supervised Hybrid Spine Network for Segmentation of Spine MR Images | Code | Arxiv |
| 2022-03 | S. Adiga V and H. Lombaert | Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation | None | Arxiv |
| 2022-03 | M. Tran and T. Peng | S5CL: Unifying Fully-Supervised, Self-Supervised, and Semi-Supervised Learning Through Hierarchical Contrastive Learning | Code | Arxiv |
| 2022-03 | M. Waerebeke and J. Dole | On the pitfalls of entropy-based uncertainty for multi-class semi-supervised segmentation | None | Arxiv |
| 2022-03 | W. Cui and R. M. Leahy | Semi-supervised Learning using Robust Loss | None | Arxiv |
| 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 |
| 2022-03 | Y. Wu and J. Cai | Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation | None | Arxiv |
| 2022-02 | Y. Hua and L. Zhang | Uncertainty-Guided Voxel-Level Supervised Contrastive Learning for Semi-Supervised Medical Image Segmentation | None | IJNS2022 |
| 2022-02 | Y. Shu and W. Li | Cross-Mix Monitoring for Medical Image Segmentation with Limited Supervision | None | TMM2022 |
| 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 |
| 2022-02 | H. Wu and J. Qin | Semi-supervised Segmentation of Echocardiography Videos via Noise-resilient Spatiotemporal Semantic Calibration and Fusion | None | MedIA2022 |
| 2022-02 | Z. Liu and C. Zhao | Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Training | None | Arxiv |
| 2022-02 | X. Zhao and G. Li | Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Segmentation | Code | ISBI2022 |
| 2022-02 | H. Basak and A. Chatterjee | An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation | Code | ISBI2022 |
| 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 |
| 2022-01 | H. Yao and X. Li | Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation | None | AAAI2022 |
| 2021-12 | S. Li and X. Yang | Semi-supervised Cardiac MRI Segmentation Based on Generative Adversarial Network and Variational Auto-Encoder | None | BIBM2021 |
| 2021-12 | N. Zhang and Y. Zhang | Semi-supervised Medical Image Segmentation with Distribution Calibration and Non-local Semantic Constraint | None | BIBM2021 |
| 2021-12 | S. Liu and G. Cao | Shape-aware Multi-task Learning for Semi-supervised 3D Medical Image Segmentation | None | BIBM2021 |
| 2021-12 | X. Xu and P. Yan | Shadow-consistent Semi-supervised Learning for Prostate Ultrasound Segmentation | Code | TMI2021 |
| 2021-12 | L. Hu and Y. Wang | Semi-supervised NPC segmentation with uncertainty and attention guided consistency | None | KBS2021 |
| 2021-12 | J. Peng and M. Pedersoli | Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels | Code | NeurIPS2021 |
| 2021-12 | Y. Xie and Y. Xia | Intra- and Inter-pair Consistency for Semi-supervised Gland Segmentation | None | TIP2021 |
| 2021-12 | M. Xu and J. Jacob | Learning Morphological Feature Perturbations for Semi-Supervised Segmentation | Code | MIDL2022 |
| 2021-12 | X. Luo and S. Zhang | Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer | Code | MIDL2022 |
| 2021-12 | Y. Zhang and J. Zhang | Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation | None | Arxiv |
| 2021-12 | J. Wang and Q. Zhou | Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotat | Code | AAAI2022 |
| 2021-12 | J. Chen and Y. Lu | MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification | Code | Arxiv |
| 2021-12 | C. Seibold and R. Stiefelhagen | Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation | None | AAAI2022 |
| 2021-11 | X. Zheng and C. Sham | Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation | None | Arxiv |
| 2021-11 | J. Peng and M. Pedersoli | Diversified Multi-prototype Representation for Semi-supervised Segmentation | Code | Arxiv |
| 2021-10 | J. Hou and J. Deng | Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations | None | WACV2021 |
| 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 |
| 2021-10 | H. Wu and J. Qin | Collaborative and Adversarial Learning of Focused and Dispersive Representations for Semi-supervised Polyp Segmentation | None | ICCV2021 |
| 2021-10 | Y. Shi and Y. Gao | Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation | Code | TMI2021 |
| 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 | TMI2021 |
| 2021-09 | K. Wang and Y. Wang | Tripled-Uncertainty Guided Mean Teacher Model for Semi-supervised Medical Image Segmentation | Code | MICCAI2021 |
| 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 |
| 2021-09 | L. Zhu and B. Ooi | Semi-Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation | Code | MICCAI2021 |
| 2021-09 | R. Zhang and G. Li | Self-supervised Correction Learning for Semi-supervised Biomedical Image Segmentation | Code | MICCAI2021 |
| 2021-09 | D. Kiyasseh and A. Chen | Segmentation of Left Atrial MR Images via Self-supervised Semi-supervised Meta-learning | None | MICCAI2021 |
| 2021-09 | Y. Wu and J. Cai | Enforcing Mutual Consistency of Hard Regions for Semi-supervised Medical Image Segmentation | None | Arxiv |
| 2021-09 | X. Zeng and Y. Wang | Reciprocal Learning for Semi-supervised Segmentation | Code | MICCAI2021 |
| 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 |
| 2021-09 | J. Chen and G. Yang | Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data | Code | TMI2021 |
| 2021-09 | X. Hu and Y. Shi | Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation | Code | MICCAI2021 |
| 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 | NeuroImage2021 |
| 2021-08 | H. Peiris and M. Harandi | Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation | Code | MICCAI2021 |
| 2021-08 | J. Sun and Y. Kong | Semi-Supervised Medical Image Semantic Segmentation with Multi-scale Graph Cut Loss | None | ICIP2021 |
| 2021-08 | X. Shen and J. Lu | PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning | None | ArXiv |
| 2021-08 | C. You and J. Duncan | SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation | None | Arxiv |
| 2021-08 | C. Li and P. Heng | Self-Ensembling Co-Training Framework for Semi-supervised COVID-19 CT Segmentation | None | JBHI2021 |
| 2021-08 | H. Yang and P. H. N. With | Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning | None | JBHI2021 |
| 2021-07 | Q. Xu and X. Wang | Semi-supervised Medical Image Segmentation with Confidence Calibration | None | IJCNN |
| 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 |
| 2021-06 | X. Liu and S. A. Tsaftaris | Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation | Code | MICCAI2021 |
| 2021-06 | P. Pandey and Mausam | Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation | None | MICCAI2021 |
| 2021-06 | C. Li and Y. Yu | Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation | None | Arxiv |
| 2021-05 | J. Xiang and S. Zhang | Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation | None | Arxiv |
| 2021-05 | S. Li and C. Guan | Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation | None | Arxiv |
| 2021-05 | C. You and J. Duncan | Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation | None | MICCAI2022 |
| 2021-05 | Z. Xie and J. Yang | Semi-Supervised Skin Lesion Segmentation with Learning Model Confidence | None | ICASSP2021 |
| 2021-04 | S. Reiß and R. Stiefelhagen | Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation | None | CVPR2021 |
| 2021-04 | S. Chatterjee and A. Nurnberger | DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data | Code | MIDL |
| 2021-04 | A. Meyer and M. Rak | Uncertainty-Aware Temporal Self-Learning (UATS): Semi-Supervised Learning for Segmentation of Prostate Zones and Beyond | Code | Arxiv |
| 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 |
| 2021-03 | Y. Zhang and C. Zhang | Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation | Code | PRCV2021 |
| 2021-03 | J. Peng and C. Desrosiers | Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization | Code | MELBA |
| 2021-03 | Y. Wu and L. Zhang | Semi-supervised Left Atrium Segmentation with Mutual Consistency Training | None | MICCAI2021 |
| 2021-02 | J. Peng and Y. Wang | Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models | None | Arxiv |
| 2021-02 | J. Dolz and I. B. Ayed | Teach me to segment with mixed supervision: Confident students become masters | Code | IPMI2021 |
| 2021-02 | C. Cabrera and K. McGuinness | Semi-supervised Segmentation of Cardiac MRI using Image Registration | None | Under review for MIDL2021 |
| 2021-02 | Y. Wang and A. Yuille | Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction | None | TMI2021 |
| 2021-02 | R. Alizadehsaniand U R. Acharya | Uncertainty-Aware Semi-supervised Method using Large Unlabelled and Limited Labeled COVID-19 Data | None | Arxiv |
| 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 |
| 2020-01 | E. Takaya and S. Kurihara | Sequential Semi-supervised Segmentation for Serial Electron Microscopy Image with Small Number of Labels | Code | Journal of Neuroscience Methods |
| 2021-01 | Y. Zhang and Z. He | Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer | None | Arxiv |
| 2020-12 | H. Wang and D. Chen | Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation | None | Arxiv |
| 2020-12 | X. Luo and S. Zhang | Efficient Semi-supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency | Code | MICCAI2021 |
| 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 |
| 2020-11 | A. Chartsias and S. A. Tsaftaris | Disentangle, Align and Fuse for Multimodal and Semi-Supervised Image Segmentation | Code | TMI2021 |
| 2020-11 | N. Horlava and N. Scherf | A comparative study of semi- and self-supervised semantic segmentation of biomedical microscopy data | None | Arxiv |
| 2020-11 | P. Wang and C. Desrosiers | Self-paced and self-consistent co-training for semi-supervised image segmentation | None | MedIA2021 |
| 2020-10 | Y. Sun and L. Wang | Semi-supervised Transfer Learning for Infant Cerebellum Tissue Segmentation | None | MLMI2020 |
| 2020-10 | L. Chen and D. Merhof | Semi-supervised Instance Segmentation with a Learned Shape Prior | Code | LABELS2020 |
| 2020-10 | S. Shailja and B.S. Manjunath | Semi supervised segmentation and graph-based tracking of 3D nuclei in time-lapse microscopy | Code | Arxiv |
| 2020-10 | L. Sun and Y. Yu | A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision | None | Arxiv |
| 2020-10 | J. Ma and X. Yang | Active Contour Regularized Semi-supervised Learning for COVID-19 CT Infection Segmentation with Limited Annotations | Code | Physics in Medicine & Biology2020 |
| 2020-10 | W. Hang and J. Qin | Local and Global Structure-Aware Entropy Regularized Mean Teacher Model for 3D Left Atrium Segmentation | Code | MICCAI2020 |
| 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 |
| 2020-10 | Y. Wang and Z. He | Double-Uncertainty Weighted Method for Semi-supervised Learning | None | MICCAI2020 |
| 2020-10 | K. Fang and W. Li | DMNet: Difference Minimization Network for Semi-supervised Segmentation in Medical Images | None | MICCAI2020 |
| 2020-10 | X. Cao and L. Cheng | Uncertainty Aware Temporal-Ensembling Model for Semi-supervised ABUS Mass Segmentation | None | TMI2020 |
| 2020-09 | Z. Zhang and W. Zhang | Semi-supervised Semantic Segmentation of Organs at Risk on 3D Pelvic CT Images | None | Arxiv |
| 2020-09 | J. Wang and G. Xie | Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions | None | BMVC2020 |
| 2020-09 | X. Luo and S. Zhang | Semi-supervised Medical Image Segmentation through Dual-task Consistency | Code | AAAI2021 |
| 2020-08 | X. Huo and Q. Tian | ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation | None | Arxiv |
| 2020-08 | Y. Xie and Y. Xia | Pairwise Relation Learning for Semi-supervised Gland Segmentation | None | MICCAI2020 |
| 2020-07 | K. Chaitanya and E. Konukoglu | Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation | Code | Arxiv |
| 2020-07 | S. Li and X. He | Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images | Code | MICCAI2020 |
| 2020-07 | Y. Li and Y. Zheng | Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation | None | MICCAI2020 |
| 2020-07 | Z. Zhao and P. Heng | Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video | Code | MICCAI2020 |
| 2020-07 | Y. Zhou and P. Heng | Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation | Code | MICCAI2020 |
| 2020-07 | A. Tehrani and H. Rivaz | Semi-Supervised Training of Optical Flow Convolutional Neural Networks in Ultrasound Elastography | None | MICCAI2020 |
| 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 |
| 2020-07 | J. Peng and C. Desrosiers | Mutual information deep regularization for semi-supervised segmentation | Code | MIDL2020 |
| 2020-07 | Y. Xia and H. Roth | Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation | None | WACV2020,MedIA2020 |
| 2020-07 | X. Li and P. Heng | Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation | Code | TNNLS2020 |
| 2020-06 | F. Garcıa and S. Ourselin | Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning | None | MICCAI2020 |
| 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 |
| 2020-05 | G. Fotedar and X. Ding | Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts | None | MICCAI2020 |
| 2020-04 | C. Liu and C. Ye | Semi-Supervised Brain Lesion Segmentation Using Training Images with and Without Lesions | None | ISBI2020 |
| 2020-04 | R. Li and D. Auer | A Generic Ensemble Based Deep Convolutional Neural Network for Semi-Supervised Medical Image Segmentation | Code | ISBI2020 |
| 2020-04 | K. Ta and J. Duncan | A Semi-Supervised Joint Learning Approach to Left Ventricular Segmentation and Motion Tracking in Echocardiography | None | ISBI2020 |
| 2020-04 | Q. Chang and D. Metaxas | Soft-Label Guided Semi-Supervised Learning for Bi-Ventricle Segmentation in Cardiac Cine MRI | None | ISBI2020 |
| 2020-04 | D. Fan and L. Shao | Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images | Code | TMI2020 |
| 2019-10 | L. Yu and P. Heng | Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation | Code | MICCAI2019 |
| 2019-10 | G. Bortsova and M. Bruijne | Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations | None | MICCAI2019 |
| 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 |
| 2019-10 | H. Zheng and X. Han | Semi-supervised Segmentation of Liver Using Adversarial Learning with Deep Atlas Prior | None | MICCAI2019 |
| 2019-10 | P. Ganayea and H. Cattin | Removing Segmentation Inconsistencies with Semi-Supervised Non-Adjacency Constraint | Code | MedIA2019 |
| 2019-10 | Y. Zhao and C. Liu | Multi-view Semi-supervised 3D Whole Brain Segmentation with a Self-ensemble Network | None | MICCAI2019 |
| 2019-10 | H. Kervade and I. Ayed | Curriculum semi-supervised segmentation | None | MICCAI2019 |
| 2019-10 | S. Chen and M. Bruijne | Multi-task Attention-based Semi-supervised Learning for Medical Image Segmentation | None | MICCAI2019 |
| 2019-10 | Z. Xu and M. Niethammer | DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation | None | MICCAI2019 |
| 2019-10 | S. Sedai and R. Garnavi | Uncertainty Guided Semi-supervised Segmentation of Retinal Layers in OCT Images | None | MICCAI2019 |
| 2019-10 | G. Pombo and P. Nachev | Bayesian Volumetric Autoregressive Generative Models for Better Semisupervised Learning | Code | MICCAI2019 |
| 2019-06 | W. Cui and C. Ye | Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model | None | IPMI2019 |
| 2019-06 | K. Chaitanya and E. Konukoglu | Semi-supervised and Task-Driven Data Augmentation | Code | IPMI2019 |
| 2019-04 | M. Jafari and P. Abolmaesumi | Semi-Supervised Learning For Cardiac Left Ventricle Segmentation Using Conditional Deep Generative Models as Prior | None | ISBI2019 |
| 2019-03 | Z. Zhao and Z. Zeng | Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation | None | BHI |
| 2019-03 | J. Peng and C. Desrosiers | Deep co-training for semi-supervised image segmentation | Code | PR2020 |
| 2019-01 | Y. Zhou and A. Yuille | Semi-Supervised 3D Abdominal Multi-Organ Segmentation via Deep Multi-Planar Co-Training | None | WACV2019 |
| 2018-10 | P. Ganaye and H. Cattin | Semi-supervised Learning for Segmentation Under Semantic Constraint | Code | MICCAI2018 |
| 2018-10 | A. Chartsias and S. Tsaftari | Factorised spatial representation learning: application in semi-supervised myocardial segmentation | None | MICCAI2018 |
| 2018-09 | X. Li and P. Heng | Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model | Code | BMVC2018 |
| 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 |
| 2017-09 | L. Gu and S. Aiso | Semi-supervised Learning for Biomedical Image Segmentation via Forest Oriented Super Pixels(Voxels) | None | MICCAI2017 |
| 2017-09 | S. Sedai and R. Garnavi | Semi-supervised Segmentation of Optic Cup in Retinal Fundus Images Using Variational Autoencoder | None | MICCAI2017 |
| 2017-09 | W. Bai and D. Rueckert | Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation | None | MICCAI2017 |
| 2016-09 | D. Mahapatra | Semi-supervised learning and graph cuts for consensus based medical image segmentation | None | PR2016 |
Some implementations of semi-supervised learning methods can be found in this Link.
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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.
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In the next two or three months, we will provide more algorithms' implementations, examples, and pre-trained models.
- If you have any questions or suggestions about this project, please contact me through email:
luoxd1996@gmail.comor QQ Group (Chinese):906808850.