Source code of this paper.
- HKBU_HPML_COVID-19
- 1. Link to Clean-CC-CCII (based on CC-CCII ver. 1.0)
- 2. Experimental Results
- 3. Citation
FTP Server: http://47.92.107.188/Datasets/COVID-DATA-CT/
Google Drive: https://drive.google.com/drive/folders/1qOWNdi5eRpuJClPimwIHvCV8z2RN7HQB?usp=sharing
Raw Data (CC-CCII) http://ncov-ai.big.ac.cn/download
If you want to run the benchmark experiments, you can refer to the directory of
covid19_pipeline
.
- The pipeline of benchmarking deep learning-based models.
- Performance comparison between different models
- Performance comparison between ResNet3d models with different depth
- Performance comparison between models trained by scan data comprising a different number of slices.
- The model accuracy before and after using MixUp data augmentation method.
The code of NAS will be released very soon ...
- NAS pipeline
- Search space
- The performance comparison between baseline models and models designed by NAS
@article {He et al.benchmark,
author = {He, Xin and Wang, Shihao and Shi, Shaohuai and Chu, Xiaowen and Tang, Jiangping and Liu, Xin and Yan, Chenggang and Zhang, Jiyong and Ding, Guiguang},
title = {Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans},
elocation-id = {2020.06.08.20125963},
year = {2020},
doi = {10.1101/2020.06.08.20125963},
publisher = {Cold Spring Harbor Laboratory Press},
URL = {https://www.medrxiv.org/content/early/2020/06/09/2020.06.08.20125963},
eprint = {https://www.medrxiv.org/content/early/2020/06/09/2020.06.08.20125963.full.pdf},
journal = {medRxiv}
}