MMViT-seg

0.Preface

This repo holds the implementation code of the paper.

*This repository provides code for "MMViT-Seg: A lightweight transformer and CNN fusion network for COVID-19 segmentation" .

1.Introduction

If you have any questions about our paper, feel free to contact us (Contact).

And if you are using COVID-SemiSeg Dataset for your research, please cite this paper (BibTeX).

2. Proposed Methods

• Preview:

  Our proposed methods consist of two key points:

  • MMViTSeg(Supervised learning with segmentation).
  • Multi query attention

2.1.Usage

1. Train
   • In the first step, you can directly run the MyTrain_LungInf_MMViT.py and just run it!
2. Test
   • When training is completed, the weights will be saved in ./Snapshots/save_weights/MMViT-Seg/.
   • Assign the path --pth_path of trained weights and --save_path of results save and in MyTest_LungInf_MMViT_for.py.
   • Just run it and results will be saved in `./Results/Lung infection segmentation/MMViT-Seg'

2.2. MMViT-Seg+ Multi-class

2.2.1. Usage

1. Train

   • Just run MyTrain_MulCls_MMViTSeg.py

2. Test

• When training is completed, the weights will be saved in ./Snapshots/save_weights/MulCls_MMViTSeg/.

• Assigning the path of weights in parameters snapshot_dir and run MyTest_MulCls_MMViTSeg.py.

3. Citation

Please cite our paper if you find the work useful:

@article{Yang2023,
title={MMViT-Seg: A lightweight transformer and CNN fusion network for COVID-19 segmentation},
author={Yang, Yuan and Zhang, Lin and Ren, Lei and Wang, Xiaohan},
journal={Computer Methods and Programs in Biomedicine},
year={2023}}

4. Citation

Please cite this paper:

@article{fan2020inf,

title={Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images},
author={Fan, Deng-Ping and Zhou, Tao and Ji, Ge-Peng and Zhou, Yi and Chen, Geng and Fu, Huazhu and Shen, Jianbing and Shao, Ling},
journal={IEEE TMI},
year={2020}
}

5. Contact

If you have any questions about our paper, Feel free to email me(yangyuan@buaa.edu.cn) .