Colloquia / Seminars / Industry Talks

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 radiotherapy outcome prediction for prostate cancer with MRI images. Validation results will be presented to demonstrate the effectiveness of our methods to deal with medical problems.

Yixuan YUAN is a postdoctoral fellow in the Department of Radiation Oncology at Stanford University, where her research centers on developing machine learning methods for brain tumor classification and radiotherapy outcome prediction. Dr. Yuan received her PhD degree in the Department of Electronic Engineering from the Chinese University of Hong Kong (CUHK) in 2016 under the supervision of Prof. Max Q.-H Meng, supported by the Hong Kong PhD Fellowship Scheme (HKPFS). Her dissertation focused on abnormality recognition and key frame extraction in Wireless capsule endoscopy Videos. Dr Yuan is an author or co-author of over 20 publications in peer-reviewed journals (including TMI, TASE, JBHI, Neuroinformatics), conference papers (MICCAI, ISBI, ICRA, IROS), and book chapters. She has received a number of research awards, including Student Research Award from the Medical and Healthcare Device Industries Association in Hong Kong; Global Scholarship Programme for Research Excellence Award from CUHK; Overseas Research Attachment Programme Award from CUHK; Toshio Fukuda Best Paper award in Mechatronics from IEEE International Conference on Mechatronics and Automation; and National Scholarship from the Chinese Ministry of Education.