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Accepted for/Published in: JMIR Dermatology

Date Submitted: Dec 8, 2021
Date Accepted: Apr 16, 2022
Date Submitted to PubMed: Oct 30, 2024

The final, peer-reviewed published version of this preprint can be found here:

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

Park C, Jeong HK, Henao R, Kheterpal MK

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

JMIR Dermatol 2022;5(2):e35497

DOI: 10.2196/35497

PMID: 39475766

PMCID: 10334885

Current Landscape of Generative Adversarial Network for Facial De-Identification in Dermatology: A Systematic Review and Evaluation

  • Christine Park; 
  • Hyeon Ki Jeong; 
  • Ricardo Henao; 
  • Meenal K. Kheterpal

ABSTRACT

Background:

De-identifying facial images is critical for protecting patient anonymity in the era of increasing tools for automatic image analysis in dermatology.

Objective:

The purpose of this paper was to review the current literature in the field of automatic facial de-identification algorithms.

Methods:

We conducted a systematic search using a combination of headings and keywords to encompass the concepts of facial de-identification and privacy preservation. The databases MEDLINE (via Pubmed), Embase (via Elsevier) and Web of Science (via Clarivate) were queried from inception to 5/1/2021. Studies of wrong design and outcomes were excluded during the screening and review process.

Results:

A total of 18 studies were included in the final review reporting various methodologies of facial de-identification algorithms. The study methods were rated individually for their utility for use cases in dermatology pertaining to skin color/pigmentation and texture preservation, data utility, and human detection. Most studies notable in the literature address feature preservation while sacrificing skin color and texture.

Conclusions:

Facial de-identification algorithms are sparse and inadequate to preserve both facial features and skin pigmentation/texture quality in facial photographs. A novel approach is needed to ensure greater patient anonymity, while increasing data access for automated image analysis in dermatology for improved patient care.


 Citation

Please cite as:

Park C, Jeong HK, Henao R, Kheterpal MK

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

JMIR Dermatol 2022;5(2):e35497

DOI: 10.2196/35497

PMID: 39475766

PMCID: 10334885

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