Is it real or not? Toward artificial intelligence-based realistic synthetic cytology image generation to augment teaching and quality assurance in pathology.

Journal: Journal of the American Society of Cytopathology
PMID: 35249862

Abstract

INTRODUCTION: Urine cytology offers a rapid and relatively inexpensive method to diagnose urothelial neoplasia. In our setting of a public sector laboratory in South Africa, urothelial neoplasia is rare, compromising pathology training in this specific aspect of cytology. Artificial intelligence-based synthetic image generation-specifically the use of generative adversarial networks (GANs)-offers a solution to this problem.

Authors

  • Ewen McAlpine
    Division of Anatomical Pathology, School of Pathology, University of the Witwatersrand.
  • Pamela Michelow
    Cytology Unit, Department of Anatomical Pathology, Faculty of Health Science, National Health Laboratory Service, University of the Witwatersrand, Johannesburg, South Africa.
  • Eric Liebenberg
    Division of Anatomical Pathology, School of Pathology, University of the Witwatersrand, Johannesburg, South Africa.
  • Turgay Celik
    Wits Institute of Data Science, University of the Witwatersrand, Johannesburg, South Africa.

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