- Fairness & Medical Imaging-based speakers and discussions
- Nov 6th, 2023, 13:00-17:00 GMT/UTC
- Fully virtual & free!
- Also see our MICCAI 2023 workshop on the same subject and last year’s iteration of this event
- Please register here: Registration form
- Zoom link for joining the workshop: Zoom link
Sanmi (Oluwasanmi) Koyejo is an Assistant Professor in the Department of Computer Science at Stanford University. Koyejo was previously an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Koyejo's research interests are in developing the principles and practice of trustworthy machine learning, focusing on applications to neuroscience and healthcare. Koyejo completed a Ph.D. at the University of Texas at Austin, and postdoctoral research at Stanford University. Koyejo has been the recipient of several awards, including a best paper award from the conference on uncertainty in artificial intelligence, a Skip Ellis Early Career Award, a Sloan Fellowship, a Terman faculty fellowship, an NSF CAREER award, a Kavli Fellowship, an IJCAI early career spotlight, and a trainee award from the Organization for Human Brain Mapping. Koyejo spends time at Google as a part of the Brain team, serves on the Neural Information Processing Systems Foundation Board, the Association for Health Learning and Inference Board, and as president of the Black in AI organization.
Sara Gerke joined Penn State Dickinson Law as assistant professor of law in July 2021. Her current research focuses on the ethical and legal challenges of artificial intelligence and big data for health care and health law in the United States and Europe. She also researches comparative law and ethics of other issues at the cutting edge of medical developments, such as the clinical translation of stem cell research, biological products, such as somatic cells, tissues, and gene therapy, reproductive medicine, such as mitochondrial replacement techniques, and digital health more generally. Professor Gerke has over 50 publications in health law and bioethics, especially AI and digital health. She was named a 2021 Health Law Scholar by the American Society for Law, Medicine & Ethics and the Saint Louis University School of Law Center for Health Law Studies. Professor Gerke previously served as a Research Fellow in Medicine, Artificial Intelligence, and Law at the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School. Before joining Harvard Law School, Professor Gerke was the General Manager of the Institute for German, European and International Medical Law, Public Health Law and Bioethics of the Universities of Heidelberg and Mannheim.
Kanwal Bhatia is the founder of Aival, a startup providing software for the evaluation of healthcare AI products. Kanwal has been working in medical imaging AI since finishing her PhD in 2007: first in developing novel algorithms then in commercialising these through industry and startups. She founded Aival with the aim of accelerating adoption of safe and robust AI, using her understanding the technical challenges involved in development - and how to test for these. Aival allows non-technical healthcare providers to rapidly validate and test radiology AI products in an independent manner, helping buyers to identify products that offer greatest clinical benefit for their patients.
All times are in UTC/GMT
|Time||Speaker and Title|
|13:10||Keynote 1: Kanwal Bhatia, Aival, UK - Medical Imaging AI Deployments: What Evidence Are We Seeing of Generalisation and Fairness?|
|13:40||Invited talk 1: Amar Kumar, McGill University, Canada - Debiasing Counterfactuals In the Presence of Spurious Correlations (MICCAI 2023 FAIMI Workshop Best Oral prize winner)|
|13:55||Invited talk 2: Frederik Pahde, Fraunhofer Heinrich-Hertz-Institute, Germany - Reveal To Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models|
|14:20||Keynote 2: Sara Gerke, Penn State University, USA - Towards Transparency: Creating Labeling Standards for AI/ML-Based Medical Devices|
|14:50||Invited talk 3: Yun-Yang Huang, National Tsing Hua University, Taiwan - Mitigating Bias in MRI-Based Alzheimer’s Disease Classifiers Through Pruning of Deep Neural Networks|
|15:05||Invited talk 4: Eran Tal, McGill University, Canada - Target Specification Bias, Counterfactual Prediction, and Algorithmic Fairness in Healthcare|
|15:30||Invited talk 5: Yongshuo Zong, University of Edinburgh, UK - MEDFAIR: Benchmarking Fairness for Medical Imaging|
|15:45||Invited talk 6: Emma Stanley, University of Calgary, Canada - A Flexible Framework for Simulating and Evaluating Biases in Deep Learning-Based Medical Image Analysis|
|16:00||Keynote 3: Sanmi Koyejo, Stanford University, USA - Algorithmic Fairness in Imaging: Where Are We Now and Where Are We Going?|
|16:30||Panel Discussion: Sara Gerke, Judy Gichoya, Kanwal Bhatia, Sanmi Koyejo, FAIMI organisers|
Aasa Feragen, DTU Compute, Technical University of Denmark
Andrew King, King’s College London
Ben Glocker, Imperial College London
Daniel Moyer, Vanderbilt University
Enzo Ferrante, CONICET, Universidad Nacional del Litoral
Eike Petersen, DTU Compute, Technical University of Denmark
Esther Puyol-Antón, HeartFlow and King’s College London
Melanie Ganz-Benjaminsen, University of Copenhagen and Neurobiology Research Unit, Rigshospitalet
Veronika Cheplygina, IT University Copenhagen
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