Automated grading of acne vulgaris by deep learning with convolutional neural networks.

Journal: Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
Published Date: Mar 1, 2020

Abstract

BACKGROUND: The visual assessment and severity grading of acne vulgaris by physicians can be subjective, resulting in inter- and intra-observer variability.

Authors

  • Ziying Vanessa Lim
    National Skin Centre, Singapore, Singapore.
  • Farhan Akram
    Department of Pathology and Clinical Bioinformatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Cuong Phuc Ngo
    Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
  • Amadeus Aristo Winarto
    Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
  • Wei Qing Lee
    School of Computing, National University of Singapore, Singapore, Singapore.
  • Kaicheng Liang
    Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
  • Hazel Hweeboon Oon
    National Skin Centre, Singapore, Singapore.
  • Steven Tien Guan Thng
    National Skin Centre, Singapore, Singapore.
  • Hwee Kuan Lee
    Daniel Aitor Holdbrook, Malay Singh, Emarene Mationg Kalaw, and Hwee Kuan Lee, Bioinformatics Institute; Malay Singh and Hwee Kuan Lee, National University of Singapore; Yukti Choudhury and Min-Han Tan, Lucence Diagnostics; Yukti Choudhury and Min-Han Tan, Institute of Bioengineering and Nanotechnology; Valerie Koh, Puay Hoon Tan, and John Yuen Shyi Peng, Singapore General Hospital; Hui Shan Tan, Ravindran Kanesvaran, and Min-Han Tan, National Cancer Center Singapore; and Hwee Kuan Lee, Institute for Infocomm Research, Singapore.

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