Math+ML+X Seminar Series

Are you passionate about Mathematics, Machine Learning, and their real-world applications? Start 2025 on an inspiring note by joining us for the final talk of the Autumn semester in the Math+ML+X series!

This seminar concludes our Autumn schedule with an insightful discussion that highlights the intersection of mathematics, machine learning, and practical applications. It’s the perfect opportunity to reflect on the progress we’ve made and look ahead to the exciting topics coming in Spring 2025.

Don’t miss this opportunity to start the year with inspiration and valuable insights. Join us! See you in Spring 2025 – we’ll be back with more engaging seminars!

Organizer：

Angelica Aviles-Rivero (YMSC)

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

摘要

AbstractWith 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.