Dark corner artefact and diagnostic performance of a market-approved neural network for skin cancer classification.

Journal: Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG
Published Date: May 10, 2021

Abstract

BACKGROUND AND OBJECTIVES: Convolutional neural networks (CNN) have proven dermatologist-level performance in skin lesion classification. Prior to a broader clinical application, an assessment of limitations is crucial. Therefore, the influence of a dark tubular periphery in dermatoscopic images (also called dark corner artefact [DCA]) on the diagnostic performance of a market-approved CNN for skin lesion classification was investigated.

Authors

  • Katharina Sies
    Department of Dermatology, University of Heidelberg, Heidelberg, Germany.
  • Julia K Winkler
    Department of Dermatology, University of Heidelberg, Heidelberg, Germany.
  • Christine Fink
  • Felicitas Bardehle
    Department of Dermatology, University of Heidelberg, Heidelberg, Germany.
  • Ferdinand Toberer
  • Felix K F Kommoss
    Department of General Pathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany.
  • Timo Buhl
  • Alexander Enk
    Department of Dermatology, University of Heidelberg, Heidelberg, Germany.
  • Albert Rosenberger
    Institute of Genetic Epidemiology at the Center of Statistics, University of Goettingen, Goettingen, Germany.
  • Holger A Haenssle
    Department of Dermatology, University of Heidelberg, Heidelberg, Germany. Electronic address: holger.haenssle@med.uni-heidelberg.de.

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