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.
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 Nationa...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 16 - 31 |
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| Main Authors: | , , , , , , , , , , , |
| Format: | diagnostic images review tables/charts Journal Article |
| Published: |
Springer Nature
Feb2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184471473&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184471473 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2025 vid: 38 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184471473 184471473 184471473 10.1007/s10278-024-01183-x 184471473 ppf: 16 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: 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. aug: au: 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 affil: PixelMed Publishing, Bangor, PA, USA sug: subj: National Cancer Institute (U.S.) Seminars and Workshops Videoconferencing Research, Medical Artificial Intelligence Utilization Diagnostic Imaging Image Processing, Computer Assisted Neoplasms Diagnosis Neoplasms Pathology Privacy and Confidentiality Histology Education, Medical Clinical Research Magnetic Resonance Imaging Minimally Invasive Procedures DICOM Cloud Computing Data Security Health Insurance Portability and Accountability Act Brain Image Interpretation, Computer Assisted ab: 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. pubtype: Academic Journal doctype: diagnostic images review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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