IEICE Transactions on Information and Systems
Online ISSN : 1745-1361
Print ISSN : 0916-8532
Regular Section
Robustness of Deep Learning Models in Dermatological Evaluation: A Critical Assessment
Sourav MISHRASubhajit CHAUDHURYHideaki IMAIZUMIToshihiko YAMASAKI
Author information
JOURNAL FREE ACCESS

2021 Volume E104.D Issue 3 Pages 419-429

Details
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.

Content from these authors
© 2021 The Institute of Electronics, Information and Communication Engineers
Previous article Next article
feedback
Top