DAY 1, May 22, 2023 (10:00 am to 2:00 pm EDT)
Welcome and Opening Comments
Keyvan Farahani, Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health
Session 1: Report of the MIDI Task Group
Report of the Medical Image De-Identification (MIDI) Task Group - Best Practices and Recommendations
Session Chair: David Clunie, PixelMed
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 2: Tools for Conventional Approaches to De-Identification
Session Chair: Fred Prior
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
Session 3: Multimodal learning in data limited contexts: Leveraging tissue-level data for understanding cell-cell interactions in cancer
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
Session 5: Improving modeling of real-world evidence data in clinical research and clinical trial design
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
Session 6: Cross-cutting discussion with session chairs
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
