Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Oct 23, 2020

Abstract

PURPOSE: High-dimensional image features that underlie COVID-19 pneumonia remain opaque. We aim to compare feature engineering and deep learning methods to gain insights into the image features that drive CT-based for COVID-19 pneumonia prediction, and uncover CT image features significant for COVID-19 pneumonia from deep learning and radiomics framework.

Authors

  • Hongmei Wang
  • Lu Wang
    Department of Laboratory, Akesu Center of Disease Control and Prevention, Akesu, China.
  • Edward H Lee
    Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, H3630, Stanford, CA, 94305.
  • Jimmy Zheng
    Department of Radiology, School of Medicine Stanford University, 725 Welch Rd MC 5654, Palo Alto, CA, 94305, USA.
  • Wei Zhang
    The First Affiliated Hospital of Nanchang University, Nanchang, China.
  • Safwan Halabi
    Department of Radiology, Stanford University, Palo Alto, California.
  • Chunlei Liu
    Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA.
  • Kexue Deng
    Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC, Hefei, 230036, Anhui, China.
  • Jiangdian Song
    Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Liaoning, Shenyang, 110819, China.
  • Kristen W Yeom
    Department of Radiology, Stanford University, Stanford, California, United States of America.

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