https://github.com/huanglizi/tfcns

This repository includes the official project of TFCNs, presented in our paper: TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation (ICANN 2022 Oral)
https://github.com/huanglizi/tfcns

cnn medical-image-analysis pytorch transformer

Last synced: 3 months ago
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This repository includes the official project of TFCNs, presented in our paper: TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation (ICANN 2022 Oral)

• Host: GitHub
• URL: https://github.com/huanglizi/tfcns
• Owner: HUANGLIZI
• License: apache-2.0
• Created: 2021-06-18T16:40:21.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2022-12-20T02:01:03.000Z (almost 3 years ago)
• Last Synced: 2023-05-02T15:24:43.324Z (over 2 years ago)
• Topics: cnn, medical-image-analysis, pytorch, transformer
• Language: Python
• Homepage:
• Size: 152 KB
• Stars: 6
• Watchers: 2
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# TFCNs (ICANN 2022 Oral)

This repository includes the official project of TFCNs, presented in our paper:  TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation

, which is accepted by ICANN 2022 (International Conference on Artificial Neural Networks).

![image](https://github.com/HUANGLIZI/TFCNs/blob/main/imgs/TFCNs.jpg)

paper link: https://arxiv.org/abs/2207.03450 or https://doi.org/10.1007/978-3-031-15937-4_65

Email: dihanli@stu.xmu.edu.cn

Please contact dihan or me if you need the further help.

# Usage

model/ : save for the model you have train

networks/ : all the component that construct our TFCNs

preprocess.py : simple data augumentation

train_utils.py : some tools used for training

utils.py : some tools used for testing

you can run the train.py and test.py for training and testing.

# Environment

Please prepare an environment with python=3.7, and then use the command "pip install -r requirements.txt" for the dependencies.

# Citation

```bash

@inproceedings{li2022tfcns,

  title={TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation},

  author={Li, Zihan and Li, Dihan and Xu, Cangbai and Wang, Weice and Hong, Qingqi and Li, Qingde and Tian, Jie},

  booktitle={International Conference on Artificial Neural Networks},

  pages={781--792},

  year={2022},

  organization={Springer}

}

```