When: Thursday, November 7, 2024, 11:00 AM - 12:00 PMWhere: Zoom
Speaker: Dr Chen Chen, School of Computer Science at the University of Sheffield
Title: Unlocking the Value of Single Modality through Multi-Modal Knowledge Transfer for Healthcare
Zoom link: https://qmul-ac-uk.zoom.us/j/81148100921
Abstract: Recent years have witnessed the remarkable success of deep neural networks in healthcare, particularly in the analysis of medical images and signals. However, their performance is often constrained by the scarcity of labelled data, driven by high labelling costs and challenges related to data privacy and sharing. In this talk, I will explore how we can overcome these limitations by leveraging multi-modal data through advanced learning frameworks to enhance the capabilities of single modality analysis. Specifically, I will present our recent and ongoing work, including those accepted at MICCAI 2023 and MICCAI 2024. This talk will delve into the details of innovative techniques such as large language model-informed pretraining and multi-modal learning for X-ray images and ECG signals, as well as demonstrating how these approaches can significantly contribute to more accurate, reliable, and cost-effective healthcare solutions.
Bio: Chen (Cherise) Chen is currently a Lecturer (assistant professor) in Computer Vision at the School of Computer Science, University of Sheffield, UK. Previously, she was a postdoc at Imperial College London (ICL) and then University of Oxford. She obtained her MSc and Ph.D. from the Department of Computing at Imperial College London in 2016 and 2022, respectively, where she worked closely with Prof. Daniel Rueckert and Dr. Wenjia Bai. Chen also has accumulated valuable industrial experience. She worked as a research scientist at Infervision Inc. in Beijing in 2017, prior to her PhD, and later as a part-time research scientist at HeartFlow, UK, in 2022 following her PhD. Her research focuses on the intersection of AI and healthcare, particularly in developing data-efficient, robust and explainable AI for clinical applications. So far, she has published more than 40 papers in leading conferences and high-impact journals on deep learning for medical data analysis such as MICCAI, ECCV, IEEE TMI and Medical Image Analysis, accumulating over 2,000 Google Scholar citations and an h-index of 20. She is a program chair for MIDL 2025; session and area chair for MICCAI 2024 and serves as lead organisers in several MICCAI workshops and challenges including, MICCAI ADSMI 2024, DALI 2023 and the CMRxMotion Challenge. Very recently, she has also been appointed as an ELLIS Scholar at the European Laboratory for Learning and Intelligent Systems in 2024. https://cherise215.github.io/.