Weakly supervised deep learning image analysis can differentiate melanoma from naevi on haematoxylin and eosin-stained histopathology slides.

Journal: Journal of the European Academy of Dermatology and Venereology : JEADV
PMID: 39215631

Abstract

BACKGROUND: The broad histomorphological spectrum of melanocytic pathologies requires large data sets to develop accurate and generalisable deep learning (DL)-based diagnostic pathology classifiers. Weakly supervised DL promotes utilisation of larger training data sets compared to fully supervised (patch annotation) approaches.

Authors

  • Nigel G Maher
    Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.
  • Homay Danaei Mehr
    Department of Computer Engineering, Faculty of Technology, Gazi University, 06500, Teknikokullar, Ankara, Turkey.
  • Cong Cong
    Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia.
  • Nurudeen A Adegoke
    Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.
  • Ismael A Vergara
    Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.
  • Sidong Liu
    Save Sight Institute, Sydney Medical School, The University of Sydney, Sydney, Australia; Brain and Mind Centre, Sydney Medical School, The University of Sydney, Sydney, Australia.
  • Richard A Scolyer
    Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.

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