Impact of hybrid supervision approaches on the performance of artificial intelligence for the classification of chest radiographs.

Journal: Computers in biology and medicine
Published Date: Mar 17, 2020

Abstract

PURPOSE: To evaluate the impact of different supervision regimens on the training of artificial intelligence (AI) in the classification of chest radiographs as normal or abnormal in a moderately sized cohort of individuals more likely to be outpatients.

Authors

  • Ryan Ellis
    San Francisco Veterans Affairs Medical Center, San Francisco Veterans Affairs Medical Center, 4150 Clement St, San Francisco, CA, 94121, USA.
  • Erik Ellestad
    Department of Radiology and Biomedical Imaging, University of California - San Francisco, 505 Parnassus Avenue M-391, San Francisco, CA, 94143, USA; San Francisco Veterans Affairs Medical Center, San Francisco Veterans Affairs Medical Center, 4150 Clement St, San Francisco, CA, 94121, USA.
  • Brett Elicker
    Department of Radiology and Biomedical Imaging, University of California - San Francisco, 505 Parnassus Avenue M-391, San Francisco, CA, 94143, USA.
  • Michael D Hope
    Department of Radiology and Biomedical Imaging, University of California - San Francisco, 505 Parnassus Avenue M-391, San Francisco, CA, 94143, USA; San Francisco Veterans Affairs Medical Center, San Francisco Veterans Affairs Medical Center, 4150 Clement St, San Francisco, CA, 94121, USA.
  • Duygu Tosun
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.

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