LongxiZhou/DLPE-method: A comprehensive platform for analyzing pulmonary parenchyma lesions on chest CT.

Type
Software
Authors
Zhou, Longxi cc
Meng, Xianglin cc
Huang, Yuxin
Kang, Kai
Zhou, Juexiao cc
Chu, Yuetan
Li, Haoyang
Xie, Dexuan
Zhang, Jiannan
Yang, Weizhen
Bai, Na
Zhao, Yi
Zhao, Mingyan
Wang, Guohua cc
Carin, Lawrence cc
Xiao, Xigang cc
Yu, Kaijiang cc
Qiu, Zhaowen cc
Gao, Xin cc
KAUST Department
Computational Bioscience Research Center (CBRC)
Computer Science
Computer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Structural and Functional Bioinformatics Group
Date
2021-08-20
Permanent link to this record
http://hdl.handle.net/10754/678660

Metadata
Show full item record
Abstract
A comprehensive platform for analyzing pulmonary parenchyma lesions on chest CT.
Citation
LongxiZhou, Arturia-Pendragon-Iris, Leihouyeung, & Lzx325. (2022). LongxiZhou/DLPE-method: DeepLungParenchymaEnhancement (Version 1.0) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.6387700
Publisher
Github
DOI
10.5281/zenodo.6387700
10.5281/zenodo.6387701
Additional Links
https://github.com/LongxiZhou/DLPE-method
Relations
Is Supplement To:
  • [Article]
    Zhou, L., Meng, X., Huang, Y., Kang, K., Zhou, J., Chu, Y., Li, H., Xie, D., Zhang, J., Yang, W., Bai, N., Zhao, Y., Zhao, M., Wang, G., Carin, L., Xiao, X., Yu, K., Qiu, Z., & Gao, X. (2022). An interpretable deep learning workflow for discovering subvisual abnormalities in CT scans of COVID-19 inpatients and survivors. Nature Machine Intelligence. https://doi.org/10.1038/s42256-022-00483-7. DOI: 10.1038/s42256-022-00483-7 Handle: 10754/678237
Collections
Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Software; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division