Federated Learning for Medical Image Computing – From Algorithm Trustworthiness to Applications

Math+ML+X Seminar Series Seminar

Organizer：

Angelica Aviles-Rivero

Speaker：

Meirui Jiang (The Chinese University of Hong Kong)

Time：

Mon., 16:00, Jan. 13, 2025

Online：

Voov (Tencent)：383-870-241

Title:

Federated Learning for Medical Image Computing – From Algorithm Trustworthiness to Applications

Abstract：

With the rapid development of big data and artificial intelligence, intelligent medical image computing has advanced significantly in recent years, becoming a revolutionary tool for enhancing clinical diagnostic and treatment efficiency. However, acquiring medical big data remains a challenging task, as individual medical institutions often struggle with limited sample sizes and incomplete case coverage.

To address these limitations, current research trends are increasingly focused on multi-center collaboration, joint training, and validation of deep learning models. Yet, the growing emphasis on patient data privacy has made multi-center data sharing more complex and restrictive. Federated learning has emerged as a promising paradigm to overcome these challenges, enabling collaborative model development across institutions. In this presentation, Dr. Jiang will discuss recent works in federated learning for medical image computing, exploring key challenges in ensuring trustworthiness, such as improving model robustness, enhancing generalization capabilities, and safeguarding privacy. The presentation will also delve into the application and validation of federated learning models using real-world clinical data.