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Searching Images for Consensus: Can AI Remove Observer Variability in Pathology?
Tizhoosh, Hamid R; Diamandis, Phedias; Campbell, Clinton J V; Safarpoor, Amir; Kalra, Shivam; Maleki, Danial; Riasatian, Abtin; Babaie, Morteza.
Affiliation
  • Tizhoosh HR; Kimia Laboratory, University of Waterloo, Waterloo, Canada. Electronic address: tzhoosh@uwaterloo.ca.
  • Diamandis P; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada.
  • Campbell CJV; Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
  • Safarpoor A; Kimia Laboratory, University of Waterloo, Waterloo, Canada.
  • Kalra S; Kimia Laboratory, University of Waterloo, Waterloo, Canada.
  • Maleki D; Kimia Laboratory, University of Waterloo, Waterloo, Canada.
  • Riasatian A; Kimia Laboratory, University of Waterloo, Waterloo, Canada.
  • Babaie M; Kimia Laboratory, University of Waterloo, Waterloo, Canada.
Am J Pathol ; 191(10): 1702-1708, 2021 10.
Article in En | MEDLINE | ID: mdl-33636179
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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Pathology / Image Processing, Computer-Assisted / Artificial Intelligence / Observer Variation Limits: Humans Language: En Journal: Am J Pathol Year: 2021 Type: Article
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Pathology / Image Processing, Computer-Assisted / Artificial Intelligence / Observer Variation Limits: Humans Language: En Journal: Am J Pathol Year: 2021 Type: Article