Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.

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

  • 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.

  • This project was originally developed for our previous works (DTC and URPC), if you find it's useful for your research, please cite the followings:

      @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}
      }
    
      @article{luo2021semi,
        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 and Chen, Jieneng and Song, Tao and  Wang, Guotai},
        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
2021-09 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 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 Arxiv
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 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

Code for semi-supervised medical image segmentation.

Some implementations of semi-supervised learning methods can be found in this Link.

Conclusion

  • This repository provides daily-update literature reviews, algorithms' implementation, and some examples of using PyTorch for semi-supervised medical image segmentation. The project is under development. Currently, it supports 2D and 3D semi-supervised image segmentation and includes five widely-used algorithms' implementations.

  • In the next two or three months, we will provide more algorithms' implementations, examples, and pre-trained models.

Questions and Suggestions

  • If you have any questions or suggestions about this project, please contact me through email: [email protected] or QQ Group (Chinese):906808850.
Owner
Healthcare Intelligence Laboratory
Healthcare Intelligence Laboratory
Comments
  • Question about MSE used here.

    Question about MSE used here.

    您好~我最近刚开始接触半监督的东西。很感谢您提供的论文及代码资源,但是在尝试的时候发现我跑train_efficient_unet_2D_mean_teacher.py这个代码的时候在有MSE那一项consistency loss的时候反而使得结果变差了,我对比了在有和没有MSE这一项(其他保持一样)的情况下,用各自最后一次迭代的模型使用test_efficient_unet_2D_acdc.py进行测试,发现在使用MSE的时候测试结果感觉已经崩溃了(使用您提供的ACDC数据)。最差的一类dice只有0.5左右。同时也发现,这个存下来的best模型通常都在迭代次数的靠前位置,而这时通常mse这一项本身就比较小,是不是mse这一项引入反而造成训练不对了。不知道您那边跑代码的过程中是否有类似的情况?或者是我运行的时候有什么需要额外注意的地方。现在都是使用默认参数直接跑了一次。

  • BRaTS2019 settings

    BRaTS2019 settings

    Hi,

    Thanks for your fantastic work. One question is why we preprocess the BRaST2019 into a binary problem. According to your paper (https://arxiv.org/pdf/2105.09511v3.pdf), the dataset has four classes (including b.g.). Did anyone do it before? Please share the reference.

    Cheers,

  • The first supplied array does not contain any binary object

    The first supplied array does not contain any binary object

    Hi, thank you so much for your efforts in implementing all these methods.

    After training on ACDC with labeled_num=3, the testing code on ACDC gives me this error "The first supplied array does not contain any binary object", which is raised near: image

    The problem is that the trained model does not predict labeled 2 and label 3, which can be seen here: image

    The visualized dice on validation set shows that there is a performance peak at 600th step image

    The test performance on the validation set (at 600th step) looks normal image

    It's confusing that on a test set image the same model does not prediction label 2 and 3 at all. Have you met this issue and could you give me some advice?

    Many thanks.

  • models

    models

    I installed all the moudels, but the model still has the problem that models are not defined. I have tried many methods. When I input from torchvision import models, the problem is solved, but this problem occurs again? How to solve it? image image image

  • No module named 'val_unet_2D_dv'

    No module named 'val_unet_2D_dv'

    Hi, I found some problem in 2ed step as following: Traceback (most recent call last): File "train_unet_2D_dv_fully_supervised.py", line 23, in <module> from val_unet_2D_dv import test_single_volume ModuleNotFoundError: No module named 'val_unet_2D_dv' I cant find the module "val_unet_2D_dv" in your repository or pypi, How can I get it?

  • About URPC Training On Brats2019

    About URPC Training On Brats2019

    Hi, Good work and thanks for the scripts, it's good to use. I've trained a URPC model on brats2019 dataset by using this script in train_brats2019_semi_seg.sh : python -u train_uncertainty_rectified_pyramid_consistency_3D.py --labeled_num 25 --total_num 250 --root_path ../data/BraTS2019 --max_iterations 30000 --base_lr 0.1 --exp BraTS2019/Uncertainty_Rectified_Pyramid_Consistency but the result is poor: 7073ed15471621f7adacf3146f4f268 The dice_score is 0.41 not like what the paper says (about 0.8). While it's OK on ACDC dataset (the dice_score is high enough and hd95 is low enough). Is anything wrong?

  • Great project, I have a question about About the cross teaching between CNN and Transformer paper....

    Great project, I have a question about About the cross teaching between CNN and Transformer paper....

    Table 2: Mean 3D DSC and HD95 (mm) on the ACDC dataset. All results are based on
    the same backbone (UNet) with a fixed seed. Mean and standard variance (in
    parentheses) are presented in this table. Red numbers denote the p-value < 0.05
    based on paired t-test when comparing with the others
    

    How to get the Mean and standard variance (in parentheses) are presented in this table? And how to generate the Red numbers denote the p-value < 0.05 based on paired t-test when comparing with the others? Could your please share the code? Thank you. @Luoxd1996 Looking forward to your reply.

  • test_urpc.py

    test_urpc.py

    Hi, Is there provide a 2D "test_urpc.py". I try to use your work:"train_uncertainty_rectified_pyramid_consistency_2D.py", but I don't know how to test it. Thanks!

  • Baidu link not working

    Baidu link not working

    Hello, thanks for your nice repository, however, I tried to download the two datasets from Baidu repository but the links are not working. Can you please look into this?

  • your results

    your results

    Thank you so much for releasing the code and dataset. Is it possible to show the performance comparison of these methods in your environment? or at least the best performance of your proposal on acdc and Brats?

  • How can we download the datasets?

    How can we download the datasets?

    Hi, I tried to download the datasets but it requires a bidu account. However, I can't register account with foreign mobile number ,and don't know much Chinese. Can you provide Google Drive, Dropbox or other download link? Thanks!

  • Differences between the URCP paper and code

    Differences between the URCP paper and code

    https://github.com/HiLab-git/SSL4MIS/blob/ad16872abd672cdcbe1ce387614797baa0a4dc6a/code/train_uncertainty_rectified_pyramid_consistency_2D.py#L165

    I noticed a log operation employed before calculating KL divergence. I was confused about it, and it is different from Eq. (4) in either the MIA paper or the MICCAI paper.

    Besides, I'm not sure whether the subscripts of Eq. (4) in both paper are wrong, because $P_C$ is not mentioned again, and $P_{avg}$ is not appear in the formulas.

  • unsupervised loss weight

    unsupervised loss weight

  • RGB input

    RGB input

    Maybe the test_single_volume function in val_2D.py also needs to be modified,where the input channel of the net is set to 1 while the model is trained with 3 channel

  • About the way you create dataloaders for labeled and unlabeled data

    About the way you create dataloaders for labeled and unlabeled data

    Hello,

    First of all congratulations for this amazing work. I want to ask how you handle the creation of dataloaders for labeled and unlabaled data. To the best of my understanding from reading the dataloaders in each iteration you forward pass the same amount of labaled and unlabaled data. Actually in each epoch you pass the whole labeled data and random sample equal amount of unlabaled data. I have read a couple of approaches for this. The first is to define an epoch as the passing of all unlabeled data from the network, but with this the labeled data will be passed from the network multiple times in an epoch. The second approach, as you have done, is to use a sampler to sample at each epoch equal amount of unlabeled data to match the size of the labeled data. Which of these 2 techniques would force the model to perform better? Generally , I'm a bit confused on how to construct the dataloaders of labeled and unlabeled data in a semi supervised setting. Any hints will be appreciated!

    Thanks in advance.

  • worker_init_fn in train_cross_teaching_between_cnn_transformer_2D.py

    worker_init_fn in train_cross_teaching_between_cnn_transformer_2D.py

    Hello , In the below line i do no not see the initialisation of worker_init_fn in the code available in the file train_cross_teaching_between_cnn_transformer_2D.py. Please can you help me with this.

    trainloader = DataLoader(db_train, batch_sampler=batch_sampler, num_workers=4, pin_memory=True, worker_init_fn=worker_init_fn)

Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation"
Pytorch Code for

Medical-Transformer Pytorch Code for the paper "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation" About this repo: This repo

Nov 30, 2022
ISBI 2022: Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image.
ISBI 2022: Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image.

Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Introduction This repository contains the PyTorch implem

Nov 9, 2022
Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

SASSnet Code for paper: Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images(MICCAI 2020) Our code is origin from UA-MT You can fin

Nov 21, 2022
A collection of loss functions for medical image segmentation
A collection of loss functions for medical image segmentation

A collection of loss functions for medical image segmentation

Nov 30, 2022
Project looking into use of autoencoder for semi-supervised learning and comparing data requirements compared to supervised learning.

Project looking into use of autoencoder for semi-supervised learning and comparing data requirements compared to supervised learning.

Dec 17, 2021
DRIFT is a tool for Diachronic Analysis of Scientific Literature.
DRIFT is a tool for Diachronic Analysis of Scientific Literature.

About DRIFT is a tool for Diachronic Analysis of Scientific Literature. The application offers user-friendly and customizable utilities for two modes:

Oct 16, 2022
2nd solution of ICDAR 2021 Competition on Scientific Literature Parsing, Task B.
2nd solution of ICDAR 2021 Competition on Scientific Literature Parsing, Task B.

TableMASTER-mmocr Contents About The Project Method Description Dependency Getting Started Prerequisites Installation Usage Data preprocess Train Infe

Nov 29, 2022
Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation
Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation

Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical images with corresponding labels, to a target image;

Oct 9, 2022
UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning
UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning

UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning This is the official PyTorch implementation for UniMoCo pape

Aug 28, 2022
[CVPR 2021] MiVOS - Mask Propagation module. Reproduced STM (and better) with training code :star2:. Semi-supervised video object segmentation evaluation.
[CVPR 2021] MiVOS - Mask Propagation module. Reproduced STM (and better) with training code :star2:. Semi-supervised video object segmentation evaluation.

MiVOS (CVPR 2021) - Mask Propagation Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang [arXiv] [Paper PDF] [Project Page] [Papers with Code] This repo impleme

Nov 18, 2022
Copy Paste positive polyp using poisson image blending for medical image segmentation
Copy Paste positive polyp using poisson image blending for medical image segmentation

Copy Paste positive polyp using poisson image blending for medical image segmentation According poisson image blending I've completely used it for bio

Oct 19, 2021
Build a medical knowledge graph based on Unified Language Medical System (UMLS)

UMLS-Graph Build a medical knowledge graph based on Unified Language Medical System (UMLS) Requisite Install MySQL Server 5.6 and import UMLS data int

Oct 23, 2022
Hybrid CenterNet - Hybrid-supervised object detection / Weakly semi-supervised object detection
Hybrid CenterNet - Hybrid-supervised object detection / Weakly semi-supervised object detection

Hybrid-Supervised Object Detection System Object detection system trained by hybrid-supervision/weakly semi-supervision (HSOD/WSSOD): This project is

Mar 21, 2022
Implementations of orthogonal and semi-orthogonal convolutions in the Fourier domain with applications to adversarial robustness
Implementations of orthogonal and semi-orthogonal convolutions in the Fourier domain with applications to adversarial robustness

Orthogonalizing Convolutional Layers with the Cayley Transform This repository contains implementations and source code to reproduce experiments for t

Nov 25, 2022
This repo holds code for TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

TransUNet This repo holds code for TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation Usage

Dec 2, 2022
Code base for "On-the-Fly Test-time Adaptation for Medical Image Segmentation"
Code base for

On-the-Fly Adaptation Official Pytorch Code base for On-the-Fly Test-time Adaptation for Medical Image Segmentation Paper Introduction One major probl

Nov 10, 2022
code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning Overview This code is for paper: Not All Unlabeled Data are Equa

Nov 23, 2022
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation (CVPR 2021)
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation (CVPR 2021)

Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation Input Image Initial CAM Successive Maps with adversar

Nov 27, 2022
[cvpr22] Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation
[cvpr22] Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation

PS-MT [cvpr22] Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation by Yuyuan Liu, Yu Tian, Yuanhong Chen, Fengbei Liu, Vasile

Nov 26, 2022