Code for this paper: Semi-supervised Medical Image Segmentation through Dual-task Consistency (DTC)
- More details and comparison methods will be released if the paper is accepted.
- The multi-classes DTC is under doing, and also will be released as we finished it.
Some important required packages include:
- Pytorch version >=0.4.1.
- TensorBoardX
- Python == 3.6
- Some basic python packages such as Numpy, Scikit-image, SimpleITK, Scipy ......
Follow official guidance to install Pytorch.
- Clone the repo:
git clone https://github.com/HiLab-git/DTC.git
cd DTC
-
Put the data in data/2018LA_Seg_Training Set.
-
Train the model
cd code
python train_la_dtc.py or python train_la_dtc_v2.py
- Test the model
python test_LA.py
Our best model is saved in the model dir DTC_model, and the pretrained SASSNet and UAMT model can be download from SASSNet_model and UA-MT_model.
If you find this repository is useful in your research, please consider to cite:
@article{luo2020semi,
title={Semi-supervised Medical Image Segmentation through Dual-task Consistency},
author={Luo, Xiangde and Chen, Jieneng and Song, Tao and Chen, Yinan and Wang, Guotai and Zhang, Shaoting},
journal={arXiv preprint arXiv:2009.04448},
year={2020}
}
- This code is adapted from UA-MT, SASSNet, SegWithDistMap.
- We thank Dr. Lequan Yu, M.S. Shuailin Li and Dr. Jun Ma for their elegant and efficient code base.
- More semi-supervised learning approaches for medical image segmentation have summarized in this repository SSL4MIS.