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_16
Snippet: Preprocessing of DeCoVNet For each CT volume, the lung masks produced by the trained UNet formed a mask volume, then the CT volume was concatenated with the mask volume to obtain a CT-Mask volume. Finally, the CT-Mask volume was resampled into a fixed spatial resolution (e.g., 224×336) without changing the number of slices for DeCoVNet training and testing. The number of slices in the whole dataset was 141±16 ranging from 73 to 250......
Document: Preprocessing of DeCoVNet For each CT volume, the lung masks produced by the trained UNet formed a mask volume, then the CT volume was concatenated with the mask volume to obtain a CT-Mask volume. Finally, the CT-Mask volume was resampled into a fixed spatial resolution (e.g., 224×336) without changing the number of slices for DeCoVNet training and testing. The number of slices in the whole dataset was 141±16 ranging from 73 to 250.
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