The Utility of Unsupervised Machine Learning in Anatomic Pathology.

Journal: American journal of clinical pathology
Published Date: Jan 6, 2022

Abstract

OBJECTIVES: Developing accurate supervised machine learning algorithms is hampered by the lack of representative annotated datasets. Most data in anatomic pathology are unlabeled and creating large, annotated datasets is a time consuming and laborious process. Unsupervised learning, which does not require annotated data, possesses the potential to assist with this challenge. This review aims to introduce the concept of unsupervised learning and illustrate how clustering, generative adversarial networks (GANs) and autoencoders have the potential to address the lack of annotated data in anatomic pathology.

Authors

  • Ewen D McAlpine
    Cytology Unit, Department of Anatomical Pathology, Faculty of Health Science, National Health Laboratory Service, University of the Witwatersrand, Johannesburg, South Africa.
  • Pamela Michelow
    Cytology Unit, Department of Anatomical Pathology, Faculty of Health Science, National Health Laboratory Service, 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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