Robustness of Deep Learning Models in Dermatological Evaluation: A Critical Assessment

• MISHRA Sourav

  University of Tokyo

• CHAUDHURY Subhajit

  University of Tokyo

• IMAIZUMI Hideaki

  exMedio Inc.

• YAMASAKI Toshihiko

  University of Tokyo

Abstract

Our paper attempts to critically assess the robustness of deep learning methods in dermatological evaluation. Although deep learning is being increasingly sought as a means to improve dermatological diagnostics, the performance of models and methods have been rarely investigated beyond studies done under ideal settings. We aim to look beyond results obtained on curated and ideal data corpus, by investigating resilience and performance on user-submitted data. Assessing via few imitated conditions, we have found the overall accuracy to drop and individual predictions change significantly in many cases despite of robust training.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E104.D (3), 419-429, 2021-03-01

  The Institute of Electronics, Information and Communication Engineers

Keywords

• deep learning
• dermatology
• interpretability
• robustness

Details

• CRID: 1390568772519791104
• NII Article ID: 130007993191
• DOI: 10.1587/transinf.2020edp7133
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E104.D/3/E104.D_2020EDP7133/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed