Selected article for: "test set and training set"
Author: Shuo Jin; Bo Wang; Haibo Xu; Chuan Luo; Lai Wei; Wei Zhao; Xuexue Hou; Wenshuo Ma; Zhengqing Xu; Zhuozhao Zheng; Wenbo Sun; Lan Lan; Wei Zhang; Xiangdong Mu; Chenxi Shi; Zhongxiao Wang; Jihae Lee; Zijian Jin; Minggui Lin; Hongbo Jin; Liang Zhang; Jun Guo; Benqi Zhao; Zhizhong Ren; Shuhao Wang; Zheng You; Jiahong Dong; Xinghuan Wang; Jianming Wang; Wei Xu
Title: AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks
  • Document date: 2020_3_23
    • ID: e6q92shw_29
    Snippet: The hospitals used different models of CT equipment from different manufacturers (see Table S2 ). Due to the shortage of CT scanners for hospitals in Wuhan, slice thicknesses varied from 0.625 mm to 10 mm, with a majority (81%) under 2 mm. We believed this variety helped to improve the generalizability of our model in real deployment. In addition, we removed the personally identifiable information (PII) from all CT scans for patients' privacy. We.....
    Document: The hospitals used different models of CT equipment from different manufacturers (see Table S2 ). Due to the shortage of CT scanners for hospitals in Wuhan, slice thicknesses varied from 0.625 mm to 10 mm, with a majority (81%) under 2 mm. We believed this variety helped to improve the generalizability of our model in real deployment. In addition, we removed the personally identifiable information (PII) from all CT scans for patients' privacy. We randomly divided the whole dataset into a training set and a test set for each model training (see Table S4 ).

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