Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19
This is an official release of the paper Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19.
Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19,
> Liansheng Wang, Jiacheng Wang, Lei Zhu, Huazhu Fu, Ping Li, Gary Cheng, Zhipeng Feng, Shuo Li, and Pheng-Ann Heng
In: IEEE Transactions on Cybernetics (T-CYB), 2023
[arXiv][Bibetex]
- [2/6 2023] We have uploaded the training codes.
- [11/10 2022] This paper has been accepted to T-CYB.
- [10/25 2022] We have created the repo.
- Network
- Pre-processing
- Pre-trained Weights
- Test codes
- Training Codes
For more details or any questions, please feel easy to contact us by email (jiachengw@stu.xmu.edu.cn).
Please download the dataset of COVID-19-P20 and MOSMED.
The file contains the pre-processing tools for both datasets. Please replace the data path with yours and then run,
$ python scripts/prepare_data.py
Before semi-training the network, you could train the basic parameters under full-supervision for the soft initialization, i.e.,
$ python scripts/train.py $PARAM
Then, you could refine the parameters using extensive unlabeled data, i.e.,
$ python scripts/semi-train.py $PARAM
Please change the $PARAM to your desired inputs.
You could download the pre-trained weights from BaiDu Disk (g2hd). Please store it locally with correct path, i.e., logs/mosmed/dmmtnet_multi_mt_0.1. Then, please run,
$ python scripts/test.py --gpu 0 --arch dmmtnet --dataset mosmeed
If you find DM2TNet useful in your research, please consider citing:
@article{wang2022dual,
title={Dual Multiscale Mean Teacher Network for Semi-Supervised Infection Segmentation in Chest CT Volume for COVID-19},
author={Wang, Liansheng and Wang, Jiacheng and Zhu, Lei and Fu, Huazhu and Li, Ping and Cheng, Gary and Feng, Zhipeng and Li, Shuo and Heng, Pheng-Ann},
journal={IEEE Transactions on Cybernetics},
year={2022},
publisher={IEEE}
}