Abstract：
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.
摘要：
用于研究的医学图像的去识别是数据共享计划的核心要求，特别是随着人工智能(AI)应用程序对数据的需求增长。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）召开了一个虚拟研讨会，旨在总结去识别技术和过程的最新技术，并探索该主题的有趣方面。本文总结了研讨会第一天的亮点,录音,以及可公开查阅的介绍。涵盖的主题包括医学影像去识别倡议（MIDI）工作组关于最佳实践和建议的报告，传统方法去识别的工具，去身份识别的国际方法，和一个行业小组。
