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

Ethics, Privacy, and Legal Issues (458) Imaging Informatics (187) Machine Learning (1400)

Published on in Vol 22, No 12 (2020): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22739, first published .
De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology

De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology

De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology

Authors of this article:

Yeon Uk Jeong1 Author Orcid Image ;   Soyoung Yoo2 Author Orcid Image ;   Young-Hak Kim3, 4 Author Orcid Image ;   Woo Hyun Shim1, 5 Author Orcid Image
Article Authors Cited by (16) Tweetations (6) Metrics

Yeon Uk Jeong   1 , BSc, PharmD ;   Soyoung Yoo   2 , PhD ;   Young-Hak Kim   3, 4 * , MD, PhD ;   Woo Hyun Shim   1, 5 * , PhD

1 Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

2 Human Research Protection Center, Asan Institute of Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

3 Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

4 Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

5 Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

*these authors contributed equally

Corresponding Author:

  • Woo Hyun Shim, PhD
  • Department of Medical Science
  • Asan Medical Institute of Convergence Science and Technology, Asan Medical Center
  • University of Ulsan College of Medicine
  • 88, Olympic-ro 43-Gil, Songpa-gu
  • Seoul, 05505
  • Republic of Korea
  • Phone: 82 2-3010-2775
  • Email: swh@amc.seoul.kr