Clunie David, Prior Fred, Rutherford Michael, Moore Stephen, Parker William, Kondylakis Haridimos, Ludwigs Christian, Klenk Juergen, Lou Bob, O'Sullivan Lawrence Tony, Marcus Dan, Dobes Jiri, Gutman Abraham, Farahani Keyvan
PixelMed Publishing, Bangor, PA, USA.
University of Arkansas for Medical Sciences, Little Rock, AR, USA.
J Imaging Inform Med. 2025 Feb;38(1):1-15. doi: 10.1007/s10278-024-01182-y. Epub 2024 Jul 12.
De-identification of medical images intended for research is a core requirement for data-sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Information Technology (CBIIT) of the US National Cancer Institute (NCI) convened a virtual workshop with the intent of summarizing the state of the art in de-identification technology and processes and exploring interesting aspects of the subject. This paper summarizes the highlights of the first day of the workshop, the recordings, and presentations of which are publicly available for review. The topics covered included the report of the Medical Image De-Identification Initiative (MIDI) Task Group on best practices and recommendations, tools for conventional approaches to de-identification, international approaches to de-identification, and an industry panel.
对用于研究的医学图像进行去识别处理是数据共享计划的一项核心要求，尤其是随着人工智能（AI）应用对数据的需求不断增长。美国国家癌症研究所（NCI）的生物医学信息学和信息技术中心（CBIIT）召开了一次虚拟研讨会，旨在总结去识别技术和流程的最新进展，并探讨该主题的有趣方面。本文总结了研讨会第一天的要点，其录音和演示文稿可公开获取以供查阅。涵盖的主题包括医学图像去识别计划（MIDI）任务组关于最佳实践和建议的报告、传统去识别方法的工具、国际去识别方法以及一个行业小组。
