By Xinggang Wang, Xianbo Deng, Qing Fu, Qiang Zhou, Jiapei Feng, Hui Ma, Wenyu Liu, Chuansheng Zheng.

This project aims at providing a deep learning algorithm to detect COVID-19 from chest CT using weak label. And the souce code of training and testing is provided. If you have interests about more details, please check our paper (IEEE Transactions on Medical Imaging).

Note: We provide an online testing website for evaluating whether a CT volume being infected, click here to test your own chest CT.

Before running the code, please prepare a computer with NVIDIA GPU, then install Anaconda, PyTorch and NVIDIA CUDA driver. Once the environment and dependent libraries are installed, please check the README.md files in 2dunet and deCoVnet directories.

• In the directory of "2dunet", the code mainly aims to segment the lung region to obtain all lung masks.

• In the directory of "deCoVnet", the code does the classification task of whether a CT volume being infected.

• In the directory of "lesion_loc", the code mainly implements the lesion localization.

• The file "20200212-auc95p9.txt" contains the output probabilities of our pretrained deCovNet on our testing set.

The pretrained models are not currently available. If you have any questions, please contact Xinggang Wang.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

You should have received a copy of the license along with this work. If not, see http://creativecommons.org/licenses/by-nc-sa/4.0/.