Abstract

Medical image analysis is going through great changes with tremendous new opportunities showing up. The rise of machine learning, the rise of big data analytics and the rise of cloud computing, have brought wonderful opportunities to invent a new generation of medical image analysis. This talk will share my experience in developing state-of-the-art methods to help physicians to analyze the huge growing medical data and make the right and early decision.

Topics of this talk will include fully-automated abnormality classification and recognition for Wireless Capsule Endoscopy (WCE) images, liver lesion detection with CT images, and brain network analysis with MRI images. Validation results will be presented to demonstrate the effectiveness of our methods to deal with medical problems.

Speaker's Biography

Yixuan YUAN is a postdoctoral fellow in the Department of Radiation Oncology at Stanford University. She received her PhD degree in the Department of Electronic Engineering from the Chinese University of Hong Kong (CUHK) in 2016, supported by the Hong Kong PhD Fellowship Scheme (HKPFS). Dr Yuan is an author or co-author of over 20 publications in peer-reviewed journals (including IEEE TMI, TCYE, TASE, JBHI, Neuroinformatics), conference papers (MICCAI, ISBI, ICRA, IROS), and book chapters. She has received a number of research awards, including Young Scientist Award from Hong Kong Institute of Science; School of Medicine Dean's Postdoctoral Fellowship of Stanford University; Student Research Award from the Medical and Healthcare Device Industries Association in Hong Kong; Toshio Fukuda Best Paper award in Mechatronics from IEEE International Conference on Mechatronics and Automation; Global Scholarship Programme for Research Excellence Award from CUHK; Overseas Research Attachment Programme Award from CUHK; and National Scholarship from the Chinese Ministry of Education.