This paper is in the following e-collection/theme issue:

Image Recognition and AI in Dermatology (11) Machine Learning (1465) Artificial Intelligence (1371) Imaging Informatics (193)

Published on in Vol 5, No 3 (2022): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35150, first published .
Assessing the Generalizability of Deep Learning Models Trained on Standardized and Nonstandardized Images and Their Performance Against Teledermatologists: Retrospective Comparative Study

Assessing the Generalizability of Deep Learning Models Trained on Standardized and Nonstandardized Images and Their Performance Against Teledermatologists: Retrospective Comparative Study

Assessing the Generalizability of Deep Learning Models Trained on Standardized and Nonstandardized Images and Their Performance Against Teledermatologists: Retrospective Comparative Study

Authors of this article:

Ayooluwatomiwa I Oloruntoba1, 2 Author Orcid Image ;   Tine Vestergaard3 Author Orcid Image ;   Toan D Nguyen4 Author Orcid Image ;   Zhen Yu2, 5 Author Orcid Image ;   Maithili Sashindranath1 Author Orcid Image ;   Brigid Betz-Stablein6 Author Orcid Image ;   H Peter Soyer6 Author Orcid Image ;   Zongyuan Ge2, 4, 7, 8 Author Orcid Image ;   Victoria Mar1, 9 Author Orcid Image
Article Authors Cited by (3) Tweetations (1) Metrics

Journals

  1. Fogelberg K, Chamarthi S, Maron R, Niebling J, Brinker T. Domain shifts in dermoscopic skin cancer datasets: Evaluation of essential limitations for clinical translation. New Biotechnology 2023;76:106 View
  2. Oloruntoba A, Ingvar Å, Sashindranath M, Anthony O, Abbott L, Guitera P, Caccetta T, Janda M, Soyer H, Mar V. Examining labelling guidelines for AI‐based software as a medical device: A review and analysis of dermatology mobile applications in Australia. Australasian Journal of Dermatology 2024;65(5):409 View