ICAI Deep-Dive Data Series III: Working with Medical Data
This is a HYBRID event.
In the medical field we are facing specific problems which makes it challenging to innovate fast. Data is a crucial ingredient for the AI experts in the medical field. With that, we do see that data use and data sharing is still not always as easy as it might seem. But on the other hand, we do see that solutions are made and we do have possibilities. Question is, how can we use those ways of working in our tech environment. Bram van Ginneken (RadboudUMC) will first discuss about FAIR requirements for data sharing. Afterwards, Gennady Roshchupkin (Erasmus MC) will talk with us about the lessons learned in his experiences of federated learning and genomics.
After the challenges we will deep dive into a discussion with the panel and public.
Moderator: Nancy Irisarri Méndez (Pyladies Amsterdam)
Part one: Challenges and background
15:00 Walk-in and networking
15:30 Challenge 1: “Sharing medical image data: how should we implement the rather vague FAIR requirements in practice” presented by Bram van Ginneken (RadboudUMC)
16:00 Challenge 2: “Everything new is well forgotten old: what we can learn from the meta-analysis studies” by Gennady Roshchupkin (Erasmus MC)
Part two: Panel Discussion
16:30 Panel Discussion on “How to tackle challenges in medical data usage by collaborating together”
- Bram van Ginneken (RadboudUMC)
- Clarisa Sanchez (University of Amsterdam & AUMC)
- Gennady Roshchupkin (Erasmus MC)
- Johan van Soest (Maastricht University)
Part three: Networking and drinks (on-site only)
Room MM 02.610 Maria Montessorigebouw
Thomas van Aquinostraat 4, 6525 GD Nijmegen
|Once you enter Maria Montessorigebouw from the main entrance, take the stairs in front of you to the 2nd floor. After arriving at the 2nd floor, go straight to the end of the corridor, then you can find room 610 on your left hand.|
About ICAI Deep-Dive:
ICAI Deep-Dive is a community meetup that focuses on technical questions and facilitates an in-depth discussions in a specific field of AI where multiple labs are working. Common challenges in research are shared across community members and labs. The aim is to stimulate knowledge exchange, creating new insights, and build bridges by sharing lessons learned, experiences, and having an open discussion.
These series have the aim to find solutions for the common challenges and issues in the community.