CVF/CVPR Medical Computer Vision Workshop
Mathias Unberath (Johns Hopkins University), Yuyin Zhou (University of California, Santa Cruz), Nicolas Padoy (University of Strasbourg, France), Tal Arbel (McGill University, Canada), Qi Dou (The Chinese University of Hong Kong), Vasileios Belagiannis (Otto von Guericke University Magdeburg)
06/19/2022 (full day)
Our workshop is featured in Computer Vision News!
The event was featured in CVPR Daily!
Our event was featured in Best of CVPR again!
The talks can still be watched below.
The CVPR MCV workshop provides a unique forum for researchers and developers in academia, industry and healthcare to present, discuss and learn about cutting-edge advances in machine learning and computer vision for medical image analysis and computer assisted interventions. The workshop offers a venue for potential new collaborative efforts, encouraging more dataset and information exchanges for important clinical applications.
The ultimate goal of the MCV workshop is to bring together stakeholders interested in leveraging medical imaging data, machine learning and computer vision algorithms to build the next generation of tools and products to advance image-based healthcare. It is time to deliver!
The program features invited talks from leading researchers from academia and industry and clinicians. There will be no paper submissions at this year's workshop.
Schedule (US Central Time)
8:00 AM , Welcome and Opening Remarks
- 8:00 AM - 10:00 AM , Session 1
- Moderators: Mathias Unberath (in-person), Nicolas Padoy (virtual), Qi Dou (virtual)
Kensaku Mori, Nagoya University, Japan (virtual)
AI-based endoscopic procedure – Current and Future
Ender Konukoglu, ETH Zurich, Switzerland (virtual)
Towards robust and trustworthy AI for medical imaging
Ben Glocker, Imperial College London, UK (virtual)
Safety nets in medical imaging AI
- Coffee Break (10:00 AM - 10:30 AM)
- 10:30 AM - 12:30 PM , Session 2
- Moderators: Vasileios Belagiannis (virtual), Yuyin Zhou (virtual)
Xiaoxiao Li, University of British Columbia, Canada (virtual)
Advancing AI in Healthcare with More and Diverse Data via Federated Learning
Ismail Ben Ayed, École de Technologie Supérieur, Canada (virtual)
Leveraging Unlabeled Data
Pablo Arbelaez, Universidad de los Andes, Colombia (in person)
AI for Global Health
Mert Sabuncu, Cornell Tech, USA (in person)
Neural Encoding Models
Lunch Break (12:30 PM - 1:30 PM)
- 1:30 PM - 3:30 PM , Session 3
- Moderators: Mathias Unberath (in person), Yuyin Zhou (virtual)
Oliver Taubmann, Siemens Healthineers, Germany (in person)
Vision in the CT Workflow
Ulas Bagci , Northwestern, USA (virtual)
Trustworthy AI for Imaging-based Diagnoses
Jie Ying Wu, Vanderbilt University, USA (in person)
Modeling robotic surgery
Daniel Rückert, TU Munich, Germany (in person)
Learning clinically useful information from medical images
3:30 PM , Closing Remarks
University of British Columbia (UBC)
Jie Ying Wu
Imperial College London
University of Los Andes (Colombia)
Ismail Ben Ayed
Ecole de Technologie Superieure (ETS)
TU Munich and Imperial College London
Kensaku Mori (Nagoya University) - "AI-based endoscopic procedure – Current and Future"
Ender Konukoglu (ETH Zurich ) - "Towards robust and trustworthy AI for medical imaging"
Ben Glocker (Imperial College London) - "Safety nets in medical imaging AI"
Xiaoxiao Li (University of British Columbia) - "Advancing AI in Healthcare with More and Diverse Data via Federated Learning"
Ismail Ben Ayed (École de Technologie Supérieur) - "Leveraging Unlabeled Data"
Pablo Arbelaez (Universidad de los Andes) - "AI for Global Health"
Mert Sabuncu (Cornell Tech) - "Neural Encoding Models"
Oliver Taubmann (Siemens Healthineers) - "Vision in the CT Workflow"
Ulas Bagci (Northwestern University) - "Trustworthy AI for Imaging-based Diagnoses"
Jie Ying Wu (Vanderbilt University, USA) - "Modeling robotic surgery"
Daniel Rückert (TU Munich) - "Learning clinically useful information from medical images"