GACDN: generative adversarial feature completion and diagnosis network for COVID-19.

Journal: BMC medical imaging
Published Date: Oct 21, 2021

Abstract

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) causes tens of million infection world-wide. Many machine learning methods have been proposed for the computer-aided diagnosis between COVID-19 and community-acquired pneumonia (CAP) from chest computed tomography (CT) images. Most of these methods utilized the location-specific handcrafted features based on the segmentation results to improve the diagnose performance. However, the prerequisite segmentation step is time-consuming and needs the intervention by lots of expert radiologists, which cannot be achieved in the areas with limited medical resources.

Authors

  • Qi Zhu
    Medical Research Center, Southwestern Hospital, Army Medical University, Chongqing 400037, P.R. China.
  • Haizhou Ye
    College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
  • Liang Sun
    College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, 211106, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, 27599, USA.
  • Zhongnian Li
    College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China. Electronic address: zhongnianli@163.com.
  • Ran Wang
    Department of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
  • Feng Shi
    Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd. Shanghai, China.
  • Dinggang Shen
    School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
  • Daoqiang Zhang
    College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.

Keywords

External Resources

Popular Topics
Recent Journals