Federated Learning in Medical Imaging: Challenges, Algorithms, and Framework

Speaker: Dr. Ziyue Xu
         Senior Scientist

Title:  "Federated Learning in Medical Imaging: Challenges, Algorithms,
         and Framework"

Date:    Tuesday, 26 March 2024

Time:    4:00pm - 5:00pm

Venue:   Room 4472 (via lift 25.26), HKUST


This talk will cover an end-to-end discussion for applying federated
learning (FL) in medical imaging -- from theoretical algorithm design to
practical framework implementation. Given the fundamentals of FL
addressing the pivotal balance between data privacy and the collaborative
enhancement of machine learning (ML) models, we will discuss the special
challenges and solutions for embedding FL in medical imaging practices,
with details of methods proposed for personalization, fairness, and
privacy protection. We will further talk about the real-life FL study
using a practical framework regarding system design and implementations.
Further, we will extend the ML models from deep learning to a more general
setting, and discuss the systematic requirements and features, especially
in the age of LLMs. Ultimately, this talk underscores the transformative
potential of FL in medical imaging, offering insights into its current
achievements and future possibilities.


Ziyue Xu is a Senior Scientist at NVIDIA, before which he was a Staff
Scientist and Lab Manager at National Institutes of Health, USA. His
research interests lie in the area of image analysis and computer vision
with applications in biomedical imaging. He has been working on medical AI
over the years along with fellow researchers and clinicians for clinical
applications. He is an IEEE Senior Member, Area Chair for major
conferences, and Associate Editor for several journals including IEEE
Transactions of Medical Imaging, and International Journal of Computer