DR-MIL: deep represented multiple instance learning distinguishes COVID-19 from community-acquired pneumonia in CT images.

Journal: Computer methods and programs in biomedicine
Published Date: Sep 9, 2021

Abstract

BACKGROUND AND OBJECTIVE: Given that the novel coronavirus disease 2019 (COVID-19) has become a pandemic, a method to accurately distinguish COVID-19 from community-acquired pneumonia (CAP) is urgently needed. However, the spatial uncertainty and morphological diversity of COVID-19 lesions in the lungs, and subtle differences with respect to CAP, make differential diagnosis non-trivial.

Authors

  • Shouliang Qi
    Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Life Science Building, 500 Zhihui Street, Hun'nan District, Shenyang, 110169, China. qisl@bmie.neu.edu.cn.
  • Caiwen Xu
    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, People's Republic of China.
  • Chen Li
    School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Bin Tian
    Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
  • Shuyue Xia
  • Jigang Ren
    Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
  • Liming Yang
    College of Science, China Agricultural University, 100083, Beijing, China. Electronic address: cauyanglm@163.com.
  • Hanlin Wang
    Department of Radiology, General Hospital of the Yangtze River Shipping, Wuhan, China. Electronic address: 75288763@qq.com.
  • Hui Yu
    Engineering Technology Research Center of Shanxi Province for Opto-Electric Information and Instrument, Taiyuan 030051, China. 13934603474@nuc.edu.cn.

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