Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification—Part 2: Pathology Whole Slide Image De-identification, De-facing, the Role of AI in Image De-identification, and the NCI MIDI Datasets and Pipeline

• Published in: Journal of digital imaging Vol. 38; no. 1; pp. 16 - 30
• Main Authors: Clunie, David, Taylor, Adam, Bisson, Tom, Gutman, David, Xiao, Ying, Schwarz, Christopher G., Greve, Douglas, Gichoya, Judy, Shih, George, Kline, Adrienne, Kopchick, Ben, Farahani, Keyvan
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2025; Springer Nature B.V
• Subjects: Artificial Intelligence; Bar codes; Bioinformatics; Biomedical data; Cancer; Data Anonymization; Datasets; Diagnostic Imaging; Humans; Identification; Imaging; Informatics; Information sharing; Information technology; Medical imaging; Medical research; Medicine; Medicine & Public Health; Metadata; Microscopy; National Cancer Institute (U.S.); Neoplasms - diagnostic imaging; Neoplasms - pathology; Pathology; Radiology; United States; Workshops; United States; De-identification; Whole slide imaging; Artificial intelligence; De-facing
• ISSN: 2948-2933; 0897-1889; 2948-2925; 2948-2933; 1618-727X
• DOI: 10.1007/s10278-024-01183-x

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 United States National Cancer Institute (NCI) convened a two half-day 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 second day of the workshop, the recordings and presentations of which are publicly available for review. The topics covered included pathology whole slide image de-identification, de-facing, the role of AI in image de-identification, and the NCI Medical Image De-Identification Initiative (MIDI) datasets and pipeline.