[New] We have transferred to a new topic about active learning and source-free domain adaptation for medical image analysis, which may be closer to the real clinical requirement. The new benchmark is here.
We are reformatting the codebase to support the 5-fold cross-validation and randomly select labeled cases, the reformatted methods in this Branch.
Recently, semi-supervised image segmentation has become a hot topic in medical image computing, unfortunately, there are only a few open-source codes and datasets, since the privacy policy and others. For easy evaluation and fair comparison, we are trying to build a semi-supervised medical image segmentation benchmark to boost the semi-supervised learning research in the medical image computing community. If you are interested, you can push your implementations or ideas to this repo or contact me at any time.
This repo has re-implemented these semi-supervised methods (with some modifications for semi-supervised medical image segmentation, more details please refer to these original works): (1) Mean Teacher; (2) Entropy Minimization; (3) Deep Adversarial Networks; (4) Uncertainty Aware Mean Teacher; (5) Interpolation Consistency Training; (6) Uncertainty Rectified Pyramid Consistency; (7) Cross Pseudo Supervision; (8) Cross Consistency Training; (9) Deep Co-Training; (10) Cross Teaching between CNN and Transformer; (11) FixMatch; (12) Regularized Dropout. In addition, several backbones networks (both 2D and 3D) are also supported in this repo, such as UNet, nnUNet, VNet, AttentionUNet, ENet, Swin-UNet, etc.
This project was originally developed for our previous works. Now and future, we are still working on extending it to be more user-friendly and support more approaches to further boost and ease this topic research. If you use this codebase in your research, please cite the following works:
@article{media2022urpc,
title={Semi-Supervised Medical Image Segmentation via Uncertainty Rectified Pyramid Consistency},
author={Luo, Xiangde and Wang, Guotai and Liao, Wenjun and Chen, Jieneng and Song, Tao and Chen, Yinan and Zhang, Shichuan, Dimitris N. Metaxas, and Zhang, Shaoting},
journal={Medical Image Analysis},
volume={80},
pages={102517},
year={2022},
publisher={Elsevier}}
@inproceedings{luo2021ctbct,
title={Semi-supervised medical image segmentation via cross teaching between cnn and transformer},
author={Luo, Xiangde and Hu, Minhao and Song, Tao and Wang, Guotai and Zhang, Shaoting},
booktitle={International Conference on Medical Imaging with Deep Learning},
pages={820--833},
year={2022},
organization={PMLR}}
@InProceedings{luo2021urpc,
author={Luo, Xiangde and Liao, Wenjun and Chen, Jieneng and Song, Tao and Chen, Yinan and Zhang, Shichuan and Chen, Nianyong and Wang, Guotai and Zhang, Shaoting},
title={Efficient Semi-supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency},
booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2021},
year={2021},
pages={318--329}}
@InProceedings{luo2021dtc,
title={Semi-supervised Medical Image Segmentation through Dual-task Consistency},
author={Luo, Xiangde and Chen, Jieneng and Song, Tao and Wang, Guotai},
journal={AAAI Conference on Artificial Intelligence},
year={2021},
pages={8801-8809}}
@misc{ssl4mis2020,
title={{SSL4MIS}},
author={Luo, Xiangde},
howpublished={\url{https://github.com/HiLab-git/SSL4MIS}},
year={2020}}| 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 | MICCAI2023 | ||||
| 2023-07 | Q. Wei and Y. Zhou | Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation | Code | MICCAI2023 | ||||
| 2023-07 | H. Peiris and M. Harandi | Uncertainty-guided dual-views for semi-supervised volumetric medical image segmentation | Code | Nature Machine Intelligence | ||||
| 2023-07 | S. Gao and S. Zhang | Correlation-Aware Mutual Learning for Semi-supervised Medical Image Segmentation | Code | MICCAI2023 | ||||
| 2023-07 | Z. Xu and R. Tong | Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation | Code | MedIA2023 | ||||
| 2023-06 | P. Liu and G. Zheng | C3PS: Context-aware Conditional Cross Pseudo Supervision for Semi-supervised Medical Image Segmentation | None | Arxiv | ||||
| 2023-05 | Z. Zhang and Z. Jiao | Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation | None | Arxiv | ||||
| 2023-05 | Z. Zhang and Z. Jiao | Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation | None | MICCAI2023 | ||||
| 2023-05 | H. Cai and Y. Gao | Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation | Code | CVPR2023 | ||||
| 2023-05 | H. Basak and Z. yin | Pseudo-Label Guided Contrastive Learning for Semi-Supervised Medical Image Segmentation | Code | CVPR2023 | ||||
| 2023-05 | Y. Wang and X. Gao | MCF: Mutual Correction Framework for Semi-Supervised Medical Image Segmentation | Code | CVPR2023 | ||||
| 2023-05 | L. Zhong and G. Wang | Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions | Code | MICCAI2023 | ||||
| 2023-05 | J. Du and T. Wang | Coarse-Refined Consistency Learning using Pixel-level Features for Semi-supervised Medical Image Segmentation | None | JBHI2023 | ||||
| 2023-05 | L. Qiu and H. Ren | Federated Semi-Supervised Learning for Medical Image Segmentation via Pseudo-Label Denoising | None | JBHI2023 | ||||
| 2023-05 | R. Aralikatti and J. Rajan | A Dual-Stage Semi-Supervised Pre-Training Approach for Medical Image Segmentation | Code | TAI2023 | ||||
| 2023-05 | Y. Zhao and J. Lu | Semi-Supervised Medical Image Segmentation With Voxel Stability and Reliability Constraints | Code | JBHI2023 | ||||
| 2023-05 | M. Xu and J. Jacob | Expectation Maximization Pseudo Labelling for Segmentation with Limited Annotations | Code | Arxiv | ||||
| 2023-05 | Y. Bai and Y. Wang | Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation | Code | CVPR2023 | ||||
| 2023-04 | H. Wu and K. Cheng | Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation | Code | Arixv | ||||
| 2023-04 | A. Lou and J. Noble | Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation | Code | TMI2023 | ||||
| 2023-04 | C.You and J. Duncan | ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast | None | Arxiv | ||||
| 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 | CVPR2023 | ||||
| 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 | H. Ni and X. Huang | Semi-supervised Body Parsing and Pose Estimation for Enhancing Infant General Movement Assessment | None | MedIA2023 | ||||
| 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 | H. Basak and A. Chatterjee | An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation | Code | ISBI2022 |
| 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 | H. Ni and X. Huang | SiamParseNet: Joint Body Parsing and Label Propagation in Infant Movement Videos | Code | MICCAI2020 | ||||
| 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.
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.
luoxd1996@gmail.com or QQ Group (Chinese):906808850.