Selected article for: "CT image and slice thickness"

Author: Min Zhou; Yong Chen; Dexiang Wang; Yanping Xu; Weiwu Yao; Jingwen Huang; Xiaoyan Jin; Zilai Pan; Jingwen Tan; Lan Wang; Yihan Xia; Longkuan Zou; Xin Xu; Jingqi Wei; Mingxin Guan; Jianxing Feng; Huan Zhang; Jieming Qu
Title: Improved deep learning model for differentiating novel coronavirus pneumonia and influenza pneumonia
  • Document date: 2020_3_30
  • ID: ilc2bzkx_10
    Snippet: In our study, the lesion regions of each CT image were annotated by two radiologists, who have more than 10 years of experience in pulmonary-thoracic disease and were aware of the clinical history of infection. We used YOLOv3 to perform lesion detection on the selected images (19) . The structure of YOLOv3 was presented on Figure E1 and detail information and CT slice thickness were presented on Table E1 and Method E2......
    Document: In our study, the lesion regions of each CT image were annotated by two radiologists, who have more than 10 years of experience in pulmonary-thoracic disease and were aware of the clinical history of infection. We used YOLOv3 to perform lesion detection on the selected images (19) . The structure of YOLOv3 was presented on Figure E1 and detail information and CT slice thickness were presented on Table E1 and Method E2.

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