Searching Images for Consensus: Can AI Remove Observer Variability in Pathology?

Journal: The American journal of pathology
Published Date: Feb 24, 2021

Abstract

One of the major obstacles in reaching diagnostic consensus is observer variability. With the recent success of artificial intelligence, particularly the deep networks, the question emerges as to whether the fundamental challenge of diagnostic imaging can now be resolved. This article briefly reviews the problem and how eventually both supervised and unsupervised AI technologies could help to overcome it.

Authors

  • Hamid R Tizhoosh
    Kimia Lab, University of Waterloo, Waterloo, ON Canada.
  • Phedias Diamandis
    Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, M5S 1A8, Canada. p.diamandis@mail.utoronto.ca.
  • Clinton J V Campbell
    McMaster University, Hamilton, ON Canada.
  • Amir Safarpoor
  • Shivam Kalra
  • Danial Maleki
    Kimia Laboratory, University of Waterloo, Waterloo, Canada.
  • Abtin Riasatian
    Kimia Laboratory, University of Waterloo, Waterloo, Canada.
  • Morteza Babaie

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