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.
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}
}
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 at here: https://digestpath2019.grand-challenge.org/Download/
- 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" ./
├── data/
│ ├── tissue-train-neg/
│ ├── tissue-train-pos-v1/
$ cd code/
$ python preprocessing.py
$ cd code/
$ python train.py --config_file='config/cac-unet-r50.yaml'