WVALE: Weak variational autoencoder for localisation and enhancement of COVID-19 lung infections.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVE: The COVID-19 pandemic is a major global health crisis of this century. The use of neural networks with CT imaging can potentially improve clinicians' efficiency in diagnosis. Previous studies in this field have primarily focused on classifying the disease on CT images, while few studies targeted the localisation of disease regions. Developing neural networks for automating the latter task is impeded by limited CT images with pixel-level annotations available to the research community.

Authors

  • Qinghua Zhou
    School of Informatics, University of Leicester, Leicester, LE1 7RH, UK.
  • Shuihua Wang
    School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.
  • Xin Zhang
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Yu-Dong Zhang
    University of Leicester, Leicester, United Kingdom.