DAY 1, May 22, 2023 (10:00 am to 2:00 pm EDT) 

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Session 3: International Approaches to De-Identification

Session chair: Dana Pe’er, Memorial Sloan Kettering Bill Parker, MD, DABR, FRCPC, University of British Columbia

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

international approaches to de-identification.

Session 3: Industry Approaches to De-Identification

Session chair: Keyvan Farahani, PhD, Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health

The priorities of and approaches by industry when it comes to protecting human identity in medical images can differ from those of governments. This session will examine those priorities and approaches with presentations by various industry groups.

Closing Remarks

David Clunie, MBBS, PixelMed, Inc.

DAY 2, May 23, 2023 (10:00 am to 2:00 pm EDT) 

Welcome and Recap

David Clunie, MBBS, PixelMed, Inc.

Session 4: Statistical Risk Analysis of Indirect Identifiers in an Image Context

Session chair: Mark Elliot, PhD, University of ManchesterSession 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

how to analyze the re-identification risk of indirect identifiers in medical images.

Session 5: De-Facing

Session chair: Ying Xiao Session 5: Improving modeling of real-world evidence data in clinical research and clinical trial designSession 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

Session 6: Cross-cutting discussion with session chairs

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