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The primary emphasis of the workshop wass on was on medical images with accompanying data elements, especially those in formats in which the data elements are embedded, particularly DICOM.

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DateSlidesRecording
5/22/2023

Day 1 Recording

5/23/2023 Slide DeckDay 2 Recording

Slide Presentations

Information about the workshop program chairs, agenda, The workshop agenda and speaker bios are at https://events.cancer.gov/nci/midi-workshopon the workshop website

Some documents on this page are not Section 508 compliant. To receive a compliant document, please email NCI CBIIT MIDI Group.

Willam International Approaches to Image De-IdentificationClosing Remarks
SpeakerPresentation Title

Slides

Day 1


Keyvan Farahani, PhD, National Heart, Lung, and Blood Institute, National Institutes of HealthWelcome & Introduction

https://docs.google.com/presentation/d/1vZQDBSNhBAZFPtJwOUE_i6NgOu6IL1ZMmIWZPaCctLA/edit?usp=sharing

Session 1: Report of the MIDI Task Group - Best Practices and Recommendations


David Clunie, MBBSDavid Clunie, PixelMedReport of the MIDI Task Grouphttps://docs.google.com/presentation/d/1pccphYb-y-uHDgicrlXZxseKMxpKNTxE51pC3ChtewA/edit

Session 2: Tools for Conventional Approaches to De-Identification

Chairperson, Fred Prior, PhD



Fred Prior, PhDFred PriorSetting the StageEmailed file
Michael Rutherford, MS, University of Arkansas for Medical SciencesThe Tools of TCIA: Standardizing Zero-Tolerance De-identificationEmailed file
Stephen Moore, MS, Washington University School of Medicine in St. LouisXNAT Platform: Image De-identificationhttps://docs.google.com/presentation/d/1roHkw48PbtV8n1agwPqtp0bBIingypxF-VNyYjI45Ss/edit?usp=sharing

Session 3: International Approaches to De-Identification

Chairperson:

William Parker, University of British Columbia



William Parker, MDMedical Data De-ID -
A Canadian Perspective
https://www.icloud.com/keynote/001sv_DNMidQcXVnlQaAvsnmw#NIH_MIDI_SessionParker Slides
Haridimos Kondylakis, PhD, Institute of Computer Science, Foundation of Research & Technology (FORTH)Data Infrastructures for AI in Medical Imaging: A report on the experiences of five EU projects
Christian Ludwigs, MSc, Aigora GmbHLegal framework and best practices for medical image de-identification in the EUChairperson: Juergen Klenk, Deloitte Consulting

Session 4: Industry Panel on Image De-Identification

Chairperson: Juergen Klenk, PhD, Deloitte Consulting



Juergen Klenk, PhDIntroductory Remarks to the Industry PanelEmailed file
Bob Lou, MD, GoogleMedical imaging de-identification on both images and text using AI modelshttps://docs.google.com/presentation/d/12TnPrlyAxNYvWbFv7y3KXZynQBHG2N5QPehQ5ah7RmY/edit#slide=id.g839f2df6fc_2_102
Also emailed file
Lawrence (Tony) O’Sullivan, MS, IBISOptimizing and Automating Radiology
Data De-identification Workflows
https://docsend.com/view/234ee2vubhyg7eyy
Dan Marcus, PhD, FlywheelThe Flywheel Platform for Intelligent Image Anonymizationhttps://docs.google.com/presentation/d/1kMd6w4w8Au3o4XjEF49y-aceg-ssTS0HKH-bsu5rZzI/edit?usp=sharing
Jiri Dobes, PhD, John Snow LabsAutomated Medical Data De-Identification and Obfuscationhttps://johnsnowlabs-my.sharepoint.com/:p:/p/jiri/EdbJq7F3wB9Lhgf6B8QH_V8BFqmuqqGVNN3NJq5XgvX9Dg?e=20s6j2
Abraham Gutman, MS, AG Mednet, Inc.Advances in Medical Imaging De-Identification and the Impact of Regulatory Constraintshttps://www.dropbox.com/s/3wa8jbrzhj6pu2s/AG%20De-ID%20Panel.pptx?dl=0David Clunie
Day 2

Session 5: Pathology Whole Slide Image De-Identification

Adam Taylor, PhD, Sage BionetworksPathology Whole Slide Image De-Identification
Tom Bisson, PhD, Charité Universitätsmedizin BerlinAnonymization of Whole Slide Images in in Histopathology for Research and Education
David Gutman, MD, PhD, Emory UniversityImage DePHI and the DSA: Open Source tools for Histology Image DeIdentification
Session 6: De-FacingDe-Facing
Ying Xiao,PhD, Hospital of the University of PennsylvaniaMedical Image De-Facing and Clinical Research Data Sharing
Christopher Schwarz, PhD, Mayo ClinicFace Recognition and De-Identification of Research Brain Images with mri_reface
Douglas Greve, PhD, MGH/HarvardMIDEFACE: Minimally Invasive Defacing
Session 7: The Role of AI in Image De-Identification

Judy Gichoya, MD, Emory University
George Shih, MD, Weill Cornell Medical CollegePixel De-Identification Using AI
Adrienne Kline, MD, PhD, Northwestern UniversityPyLogik: An open-source resource for medical image de-identification
Session 8: NCI MIDI Datasets and Pipeline

Keyvan Farahani, PhDThe Medical Image De-Identification Initiative (MIDI)
Fred Prior, PhDSynthetic Data for De-Identification Testing -
The MIDI Datasets
Ben Kopchick, PhD, Deloitte ConsultingBuilding a cloud-based MIDI pipeline