TL;DR

Scope

As AI transforms healthcare, ensuring fairness in medical imaging is crucial to prevent health disparities. This tutorial provides a comprehensive introduction to algorithmic fairness, covering key concepts, sources of bias, and practical mitigation strategies. Through expert-led lectures and hands-on demonstrations, participants will explore real-world case studies, fairness evaluation techniques, and tools for bias assessment. By the end, attendees will be equipped to integrate fairness principles into their machine learning workflows, fostering more equitable AI in healthcare.

Schedule

Time Speaker and Title
08:00 - 10:00 Lecture on Fairness of AI in Medical Imaging
10:00 - 10:30 Coffee Break
10:30 - 12:30 Hands-on session on Fairness of AI in Medical Imaging

Organizers

Aasa Feragen, DTU Compute, Technical University of Denmark
Andrew King, King’s College London
Ben Glocker, Imperial College London
Enzo Ferrante, CONICET, Universidad Nacional del Litoral
Eike Petersen, Fraunhofer Institute for Digital Medicine MEVIS
Esther Puyol-Antón, HeartFlow and King’s College London
Melanie Ganz-Benjaminsen, University of Copenhagen & Neurobiology Research Unit, Rigshospitalet
Veronika Cheplygina, IT University Copenhagen
Tareen Dawood, DTU Compute, Technical University of Denmark
Joris Fournel, DTU Compute, Technical University of Denmark
Miguel Lopez Perez, DTU Compute, Technical University of Denmark

Contact

Please direct any inquiries related to the workshop or this website to faimi-organizers@googlegroups.com.