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

Innovations in Dermatological Electronic Health Records and Documentation (3) Imaging Informatics (302) Machine Learning (2076) Digital Photography and Imaging in Dermatology (43) Machine Learning from Digital Images in Dermatology (17)

Published on in Vol 3, No 1 (2020): Jan-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18149, first published .
LesionMap: A Method and Tool for the Semantic Annotation of Dermatological Lesions for Documentation and Machine Learning

LesionMap: A Method and Tool for the Semantic Annotation of Dermatological Lesions for Documentation and Machine Learning

LesionMap: A Method and Tool for the Semantic Annotation of Dermatological Lesions for Documentation and Machine Learning

Authors of this article:

Bell Raj Eapen1 Author Orcid Image ;   Norm Archer1 Author Orcid Image ;   Kamran Sartipi2 Author Orcid Image
Article Authors Cited by (4) Tweetations (4) Metrics

Journals

  1. Eapen B, Kaliyadan F, Ashique K. DICODerma: A Practical Approach for Metadata Management of Images in Dermatology. Journal of Digital Imaging 2022;35(5):1231 View
  2. Eapen B. Artificial intelligence in dermatology: A practical introduction to a paradigm shift. Indian Dermatology Online Journal 2020;11(6):881 View

Books/Policy Documents

  1. Mannai Z, Kalboussi A, Kacem A. Participative Urban Health and Healthy Aging in the Age of AI. View

Conference Proceedings

  1. Sovetsky A, Matveyev A, Zykov A, Zaitsev V, Matveev L, Luo Q, Li X, Gu Y, Zhu D. Optics in Health Care and Biomedical Optics XIII. OCT-specific signal features for semi-automatic semantic scans annotation and segmentations View