Reproducible Naevus Counts Using 3D Total Body Photography and Convolutional Neural Networks.

Journal: Dermatology (Basel, Switzerland)
Published Date: Jan 1, 2022

Abstract

BACKGROUND: The number of naevi on a person is the strongest risk factor for melanoma; however, naevus counting is highly variable due to lack of consistent methodology and lack of inter-rater agreement. Machine learning has been shown to be a valuable tool for image classification in dermatology.

Authors

  • Brigid Betz-Stablein
    QIMR Berghofer Medical Research Institute, Cancer and Population Studies, Brisbane, Queensland, Australia.
  • Brian D'Alessandro
    Canfield Scientific Inc., Fairfield, New Jersey, USA.
  • Uyen Koh
    The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Queensland, Australia.
  • Elsemieke Plasmeijer
    QIMR Berghofer Medical Research Institute, Cancer and Population Studies, Brisbane, Queensland, Australia.
  • Monika Janda
    Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
  • Scott W Menzies
    Sydney Medical School, The University of Sydney, Camperdown, New South Wales, Australia.
  • Rainer Hofmann-Wellenhof
    Department of Dermatology, Medical University Graz, Graz, Austria.
  • Adele C Green
    QIMR Berghofer Medical Research Institute, Cancer and Population Studies, Brisbane, Queensland, Australia.
  • H Peter Soyer
    Dermatology Research Centre, The University of Queensland, The University of Queensland Diamantina Institute, Brisbane, Australia.

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