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The Medical Imaging De-Identification workshop is being offered to examine the state of the science for the de-identification of publicly released medical images. The primary emphasis is on medical images with accompanying data elements, especially those encoded in formats in which the data elements are embedded, particularly DICOM. The sessions described below make up the workshop.
Please register here.
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Keyvan Farahani, Center for Biomedical Informatics and Information Technology, , PhD, National Heart, Lung, and Blood Institute & National Cancer Institute, National Institutes of Health
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In this session, David Clunie, chair of the MIDI Task Group, will summarize the best practices and recommendations included in the task group's report, recently available in pre-print, followed by a question and answer period.
Session Chair: David Clunie, MBBS, PixelMed
10:10 am - 10:50 am David Clunie, MBBS, PixelMed
Summary of the MIDI Task Group Report
10:50 am - 11:00 am Discussion
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In this session, researchers will discuss the use of large-scale perturbation data for causal modeling, combining representation learning with perturbation approaches, and methods to extrapolate beyond existing perturbation data.
Speakers:
Yoshua Bengio, Université de Montréal
GV Shivashankar, ETH Zurich
Smita Krishnaswamy, Yale
Panelists:
Paquita Vazquez, Broad Institute
Byung-Jun Yoon, Texas A&M University and Brookhaven National Laboratory
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speakers will share methods currently in use for medical de-identification.
Session Chair: Fred Prior, PhD, University of Arkansas for Medical Sciences
11:00 am - 11:10 am Fred Prior, PhD, University of Arkansas for Medical Sciences
Setting the Stage
11:10 am - 11:20 am Michael Rutherford, MS, University of Arkansas for Medical Sciences
The Tools of TCIA: Standardizing Zero-Tolerance De-identification
11:20 am - 11:30 am Stephen Moore, MS, Washington University School of Medicine in St. Louis
XNAT Platform: Image De-identification
11:30 am - 11:50 am Panel Discussion
This session will focus on requirements for de-identification outside of the United States.
Session chair: William Parker, MD, University of British Columbia
12:00 pm - 12:10 pm William Parker, MD, University of British Columbia
12:10 pm - 12:20 pm 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
12:20 pm - 12:30 pm Christian Ludwigs, MSc, Aigora GmbH
Legal Framework and Best Practices for Medical Image De-Identification in the EU
12:30 pm - 12:40 pm Discussion
This session, largely a panel discussion, will feature flash presentations by industry groups and discuss their innovative approaches to image de-identification.
Session chair: Juergen Klenk, PhD, Deloitte Consulting
Panelists:
David Clunie, MBBS, PixelMed
David Clunie, MBBS, PixelMed
In this session, researchers will discuss pathology whole slide image de-identification.
Session chair: Adam Taylor, PhD, Sage Bionetworks
10:10 am - 10:20 am Adam Taylor, PhD, Sage Bionetworks
10:20 am - 10:30 am Tom Bisson, PhD, Charité Universitätsmedizin Berlin
Anonymization of Whole Slide Images in in Histopathology for Research and Education
10:30 am - 10:40 am David Gutman, MD, PhD, Emory University
Image DePHI and the DSA: Open Source tools for Histology Image De-Identification
10:40 am - 11:00 am Panel Discussion
This session will focus on balancing the risks of removing potentially reconstructable facial information in head and neck cross-sectional images, also called de-facing, with the diminished utility of these images caused by restricted access to them.
Session chair: Ying Xiao, PhD, Hospital of the University of Pennsylvania
11:00 am - 11:10 am Ying Xiao, PhD, University of Pennsylvania
11:10 am - 11:20 am Christopher Schwarz, PhD, Mayo Clinic
Face Recognition and De-Identification of Research Brain Images with mri_reface
11:20 am - 11:30 am Douglas Greve, PhD, MGH/Harvard
MIDEFACE: Minimally Invasive Defacing
11:30 am - 11:50 am Panel Discussion
In this session, researchers discuss the utility of AI algorithms in image de-identification.
Session chair: Judy Wawira Gichoya, MD, Emory University
12:00 pm - 12:10 pm Judy Wawira Gichoya, MD, Emory University
12:10 pm - 12:20 pm George Shih, MD, Weill Cornell Medical College
Pixel De-Identification Using AI
12:20 pm - 12:30 pm Adrienne Kline, MD, PhD, Northwestern University
PyLogik: An open-source resource for medical image de-identification
12:30 pm - 12:50 pm Panel Discussion
This session will present CBIIT/NCI Medical Image De-Identification Datasets and Pipeline
Session chair: Keyvan Farahani, PhD, National Heart, Lung, and Blood Institute & National Cancer Institute, National Institutes of Health
12:50 pm - 1:00 pm Keyvan Farahani, PhD
1:00 pm - 1:10 pm Fred Prior, PhD, University of Arkansas for Medical Sciences
Synthetic Data for De-Identification Testing
The MIDI Datasets
1:10 pm - 1:20 pm Ben Kopchick, PhD, Deloitte Consulting
Building a cloud-based MIDI pipeline
1:20 pm - 1:50 pm Panel Discussion
David Clunie, MBBS, PixelMed
Keyvan Farahani, PhD, NHLBI & NCI, NIH
Session chair: Dana Pe’er, Memorial Sloan Kettering
This session will focus on multimodal learning in data limited contexts, including cell-cell interactions and predicting outcomes. Dealing with imbalances across multimodal data sets and foundational models will also be discussed.
Speakers:
Elena Fertig, Johns Hopkins
Elham Azizi, Columbia
Livnat Jerby, Stanford
Panelists:
Marianna Rapsomaniki, IBM Research
Arjun Krishnan, University of Colorado
DAY 2, April 4, 2023 (11 am to 3:30 pm EDT)
Session 4: Making use of large-scale, structured clinical research data and image repositories
Session chair: Ziad Obermeyer, UC Berkeley
In this session, researchers will discuss the use of large-scale clinical research data for machine learning models. Discussion topics include the use of synthetic data, considerations of bias, generalizable models, and development of digital twins.
Speakers:
Chris Probert, InSitro
James Zou, Stanford
Mihaela van der Schaar, University of Cambridge
Panelists:
Lily Peng, Verily
Matthew Lungren, Microsoft/UCSF
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Session chair: Tianxi Cai, Harvard
This session will focus on real-world evidence (RWE) data modeling, including issues associated with RWE data such as electronic health record coding and unbalanced data, towards the development of clinical trials.
Speakers:
Sean Khozin, MIT
Limor Appelbaum, Beth Israel Deaconess
Ryan Copping, Genentech
Panelists:
Donna Rivera, FDA
Khaled El Emam, University of Ottawa
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Session chair: Olivier Gevaert, Stanford University
Discussion of the approaches and challenges identified during the workshop and opportunities for the future.
Panelists:
Caroline Uhler, MIT and Broad Institute
Trey Ideker, UCSD
Dana Pe’er, Memorial Sloan Kettering
Ziad Obermeyer, UC Berkeley
Tianxi Cai, Harvard
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