Unsupervised Machine Learning in Pathology: The Next Frontier.

Journal: Surgical pathology clinics
PMID: 32389272

Abstract

Applications of artificial intelligence and particularly deep learning to aid pathologists in carrying out laborious and qualitative tasks in histopathologic image analysis have now become ubiquitous. We introduce and illustrate how unsupervised machine learning workflows can be deployed in existing pathology workflows to begin learning autonomously through exploration and without the need for extensive direction. Although still in its infancy, this type of machine learning, which more closely mirrors human intelligence, stands to add another exciting layer of innovation to computational pathology and accelerate the transition to autonomous pathologic tissue analysis.

Authors

  • Adil Roohi
    Harvard Extension School, 51 Brattle Street, Cambridge, MA 02138, USA; Princess Margaret Cancer Centre, 101 College Street, Toronto, Ontario M5G 1L7, Canada.
  • Kevin Faust
    Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada.
  • Ugljesa Djuric
    Laboratory Medicine Program, Department of Pathology, University Health Network, 200 Elizabeth Street, Toronto, ON, M5G 2C4, Canada.
  • Phedias Diamandis
    Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, M5S 1A8, Canada. p.diamandis@mail.utoronto.ca.

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