Current Landscape of Generative Adversarial Networks for Facial Deidentification in Dermatology: Systematic Review and Evaluation
BackgroundDeidentifying facial images is critical for protecting patient anonymity in the era of increasing tools for automatic image analysis in dermatology. ObjectiveThe aim of this paper was to review the current literature in the field of automatic facial deid...
Main Authors: | Christine Park, Hyeon Ki Jeong, Ricardo Henao, Meenal Kheterpal |
---|---|
Format: | Article |
Language: | English |
Published: |
JMIR Publications
2022-05-01
|
Series: | JMIR Dermatology |
Online Access: | https://derma.jmir.org/2022/2/e35497 |
Similar Items
-
Deep Learning in Dermatology: A Systematic Review of Current Approaches, Outcomes, and Limitations
by: Hyeon Ki Jeong, et al.
Published: (2023-01-01) -
Consent and Deidentification of Patient Images in Dermatology Journals: Observational Study
by: Japbani K Nanda, et al.
Published: (2022-07-01) -
Lesion identification and malignancy prediction from clinical dermatological images
by: Meng Xia, et al.
Published: (2022-09-01) -
k-Same-Net: k-Anonymity with Generative Deep Neural Networks for Face Deidentification
by: Blaž Meden, et al.
Published: (2018-01-01) -
Privacy in context : the costs and benefits of a new deidentification method
by: Trepetin, Stanley
Published: (2007)