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

Reviews in Dermatology (41) Image Recognition and AI in Dermatology (20)

Published on in Vol 5, No 2 (2022): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35497, first published .
Current Landscape of Generative Adversarial Networks for Facial Deidentification in Dermatology: Systematic Review and Evaluation

Current Landscape of Generative Adversarial Networks for Facial Deidentification in Dermatology: Systematic Review and Evaluation

Current Landscape of Generative Adversarial Networks for Facial Deidentification in Dermatology: Systematic Review and Evaluation

Authors of this article:

Christine Park1 Author Orcid Image ;   Hyeon Ki Jeong1 Author Orcid Image ;   Ricardo Henao2 Author Orcid Image ;   Meenal Kheterpal1 Author Orcid Image
Article Authors Cited by (3) Tweetations (4) Metrics

Journals

  1. Ahmed H. Uncover This Tech Term: Generative Adversarial Networks. Korean Journal of Radiology 2024;25(5):493 View
  2. Ning Y, Teixayavong S, Shang Y, Savulescu J, Nagaraj V, Miao D, Mertens M, Ting D, Ong J, Liu M, Cao J, Dunn M, Vaughan R, Ong M, Sung J, Topol E, Liu N. Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist. The Lancet Digital Health 2024;6(11):e848 View

Conference Proceedings

  1. Park S, Kim H, Choi S, Kim T, Park E. Proceedings of the 4th Workshop on Security Implications of Deepfakes and Cheapfakes. Privacy-Driven Faces: A Survey on Generative Facial De-identification View