Multi-level-colonoscopy-malignant-tissue-detection-with-adversarial-CAC-UNet 
Implementation detail for our paper “Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet”
DigestPath 2019
The proposed scheme in this paper achieves the best results in MICCAI DigestPath2019 challenge (https://digestpath2019.grand-challenge.org/Home/) on colonoscopy tissue segmentation and classification task.
Dataset
Description of dataset can be found here:
https://digestpath2019.grand-challenge.org/Dataset/
To download the the DigestPath2019 dataset, please sign the DATABASE USE AGREEMENT first and download the dataset at here.
If you have problems about downing the dataset, please contact Prof. Hongsheng Li:[email protected] and refer to the following link:
https://digestpath2019.grand-challenge.org/Download/
Image sample:
Envs
- Pytorch 1.0
- Python 3+
- cuda 9.0+
install
$ pip install -r requirements.txt
apex : Tools for easy mixed precision and distributed training in Pytorch
$ git clone https://github.com/NVIDIA/apex
$ cd apex
$ pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
Dataset
├── data/
│ ├── tissue-train-neg/
│ ├── tissue-train-pos-v1/
Preprocessing
$ cd code/
$ python preprocessing.py
Training
$ cd code/
$ python train.py --config_file='config/cac-unet-r50.yaml'
Citation
Please cite this paper in your publications if it helps your research:
@article{zhu2021multi,
title={Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet},
author={Zhu, Chuang and Mei, Ke and Peng, Ting and Luo, Yihao and Liu, Jun and Wang, Ying and Jin, Mulan},
journal={Neurocomputing},
volume={438},
pages={165--183},
year={2021},
publisher={Elsevier}
}
About the multi-level adversarial segmentation part, you can read our ICASSP paper for more details:
@inproceedings{mei2020cross,
title={Cross-stained segmentation from renal biopsy images using multi-level adversarial learning},
author={Mei, Ke and Zhu, Chuang and Jiang, Lei and Liu, Jun and Qiao, Yuanyuan},
booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={1424--1428},
year={2020},
organization={IEEE}
}
The challenge paper DigestPath: a Benchmark Dataset with Challenge Review for the Pathological Detection and Segmentation of Digestive-System should be also cited:
@article{da2022digestpath,
title={DigestPath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system},
author={Da, Qian and Huang, Xiaodi and Li, Zhongyu and Zuo, Yanfei and Zhang, Chenbin and Liu, Jingxin and Chen, Wen and Li, Jiahui and Xu, Dou and Hu, Zhiqiang and others},
journal={Medical Image Analysis},
volume={80},
pages={102485},
year={2022},
publisher={Elsevier}
}
Author
Ke Mei, Ting Peng, Chuang Zhu
If you have any questions, you can contact me directly.
评论