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).
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}
}
```