HiLab-git / Ssl4mis
Licence: mit
Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.
Stars: ✭ 336
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Semi-supervised-learning-for-medical-image-segmentation.
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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 repository at any time.
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This project was originally developed for our previous works, if you find it's useful for your research, please consider to cite the followings:
@article{luo2020urpc, title={Efficient Semi-supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency}, author={Luo, Xiangde and Liao, Wenjun and Chen, Jieneng and Song, Tao and Chen, Yinan and and Zhang, Shichuan and Chen, Nianyong and Wang, Guotai and Zhang, Shaoting}, journal={arXiv preprint arXiv:2012.07042}, year={2020} } @article{luo2020semi, 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} } @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 |
|---|---|---|---|---|
| 2021-03 | Y. Zhang and C. Zhang | Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation | Code | Arxiv |
| 2021-03 | J. Peng and C. Desrosiers | Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization | None | MELBA |
| 2021-03 | Y. Wu and L. Zhang | Semi-supervised Left Atrium Segmentation with Mutual Consistency Training | None | Arxiv |
| 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 | Semi-supervised Segmentation via Uncertainty Rectified Pyramid Consistency and Its Application to Gross Target Volume of Nasopharyngeal Carcinoma | Code | Arxiv |
| 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 | 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 | Arxiv |
| 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 | 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 |
Code for semi-supervised medical image segmentation.
Some implementations of semi-supervised learning methods can be found in this Link.
Conclusion
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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.
Questions and Suggestions
- If you have any questions or suggestions about this project, please contact me through email:
[email protected]or QQ Group (Chinese):906808850.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].