Author: Chuansheng Zheng; Xianbo Deng; Qing Fu; Qiang Zhou; Jiapei Feng; Hui Ma; Wenyu Liu; Xinggang Wang
Title: Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label Document date: 2020_3_17
ID: ll4rxd9p_25
Snippet: This was a retrospective case series study and no patients were involved in the study design, setting the research questions, or the outcome measures directly. No patients were asked to advise on interpretation or writing up of results. The software for COVID-19 detection with the pre-trained model as well as the results was available at https://github.com/sydney0zq/covid-19-detection, which will be made publicly available on the publication of t.....
Document: This was a retrospective case series study and no patients were involved in the study design, setting the research questions, or the outcome measures directly. No patients were asked to advise on interpretation or writing up of results. The software for COVID-19 detection with the pre-trained model as well as the results was available at https://github.com/sydney0zq/covid-19-detection, which will be made publicly available on the publication of this paper. Training DeCoVNet on the training set which consisted of 499 CT volumes took about 20 hours (11 hours for UNet and 9 hours for DeCoVNet) and testing a CT volume costed an average of 1.93 seconds (1.80 seconds for UNet and 0.13 seconds for DeCoVNet) on an NVIDIA Titan Xp GPU.
Search related documents:
Co phrase search for related documents- case series and outcome measure: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
- case series and paper publication: 1
- case series and research question: 1, 2, 3, 4, 5, 6, 7
- case series study and interpretation advise: 1
- case series study and outcome measure: 1
- case series study and research question: 1, 2, 3
- CT volume and outcome measure: 1, 2, 3
- interpretation advise and research question: 1
Co phrase search for related documents, hyperlinks ordered by date