Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19.

Journal: Journal of translational medicine
Published Date:

Abstract

BACKGROUND: Coronavirus disease 2019 (COVID-19) is very contagious. Cases appear faster than the available Polymerase Chain Reaction test kits in many countries. Recently, lung computerized tomography (CT) has been used as an auxiliary COVID-19 testing approach. Automatic analysis of the lung CT images is needed to increase the diagnostic efficiency and release the human participant. Deep learning is successful in automatically solving computer vision problems. Thus, it can be introduced to the automatic and rapid COVID-19 CT diagnosis. Many advanced deep learning-based computer vison techniques were developed to increase the model performance but have not been introduced to medical image analysis.

Authors

  • Daryl L X Fung
    Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada.
  • Qian Liu
    State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
  • Judah Zammit
    Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada.
  • Carson Kai-Sang Leung
    Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada.
  • Pingzhao Hu
    c Department of Biochemistry and Medical Genetics , University of Manitoba , Winnipeg , MB , Canada.