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

Journals

  1. Chlap P, Min H, Vandenberg N, Dowling J, Holloway L, Haworth A. A review of medical image data augmentation techniques for deep learning applications. Journal of Medical Imaging and Radiation Oncology 2021;65(5):545 View
  2. Wang L, Zhao Q, Kumar V. Deformation Analysis and Research of Building Envelope by Deep Learning Technology under the Reinforcement of the Diaphragm Wall. Computational Intelligence and Neuroscience 2022;2022:1 View
  3. Yang H, Rahmanti A, Huang C, Li Y. How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes?. Journal of Medical Internet Research 2022;24(3):e29506 View
  4. Karatas M, Eriskin L, Deveci M, Pamucar D, Garg H. Big Data for Healthcare Industry 4.0: Applications, challenges and future perspectives. Expert Systems with Applications 2022;200:116912 View
  5. Li G, Wu X, Ma X. Artificial intelligence in radiotherapy. Seminars in Cancer Biology 2022;86:160 View
  6. Wahid K, Glerean E, Sahlsten J, Jaskari J, Kaski K, Naser M, He R, Mohamed A, Fuller C. Artificial Intelligence for Radiation Oncology Applications Using Public Datasets. Seminars in Radiation Oncology 2022;32(4):400 View
  7. Han W, Han X, Zhou S, Zhu Q. The Development History and Research Tendency of Medical Informatics: Topic Evolution Analysis. JMIR Medical Informatics 2022;10(1):e31918 View
  8. Sahlsten J, Wahid K, Glerean E, Jaskari J, Naser M, He R, Kann B, Mäkitie A, Fuller C, Kaski K. Segmentation stability of human head and neck cancer medical images for radiotherapy applications under de-identification conditions: Benchmarking data sharing and artificial intelligence use-cases. Frontiers in Oncology 2023;13 View
  9. Neri E, Aghakhanyan G, Zerunian M, Gandolfo N, Grassi R, Miele V, Giovagnoni A, Laghi A. Explainable AI in radiology: a white paper of the Italian Society of Medical and Interventional Radiology. La radiologia medica 2023;128(6):755 View
  10. Pervan B, Tomic S, Ivandic H, Knezovic J. MIDOM—A DICOM-Based Medical Image Communication System. Applied Sciences 2023;13(10):6075 View
  11. Filippi C, Stein J, Wang Z, Bakas S, Liu Y, Chang P, Lui Y, Hess C, Barboriak D, Flanders A, Wintermark M, Zaharchuk G, Wu O. Ethical Considerations and Fairness in the Use of Artificial Intelligence for Neuroradiology. American Journal of Neuroradiology 2023;44(11):1242 View
  12. Uchida T, Kin T, Sato K, Koike T, Kiyofuji S, Takeda Y, Niwa R, Saito T, Takashima I, Kawahara T, Miyawaki S, Oyama H, Saito N. Reproducibility of Facial Information in Three-Dimensional Reconstructed Head Images: An Exploratory Study. Current Medical Imaging Formerly Current Medical Imaging Reviews 2023;19(12) View
  13. Gao C, Landman B, Prince J, Carass A. Reproducibility evaluation of the effects of MRI defacing on brain segmentation. Journal of Medical Imaging 2023;10(06) View
  14. Patel R, Provenzano D, Loew M. Anonymization and validation of three-dimensional volumetric renderings of computed tomography data using commercially available T1-weighted magnetic resonance imaging-based algorithms. Journal of Medical Imaging 2023;10(06) View
  15. Ryu D, Lee C, Lee H, Shim Y, Hong Y, Cho J, Kim S, Lee J, Yang D. Assessing the Impact of Defacing Algorithms on Brain Volumetry Accuracy in MRI Analyses. Dementia and Neurocognitive Disorders 2024;23(3):127 View

Books/Policy Documents

  1. Lien C, Deng R, Fuh J, Ting Y, Yang A. Medical Image and Signal Analysis in Brain Research. View
  2. Malhotra A, Malhotra A, Bernstein M. Ethical Challenges for the Future of Neurosurgery. View