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【医学部学术讲座】——Machine learning + Knowledge modeling for medical image recognition, segmentation, parsing

文章来源: 作者: 发布时间:2018年10月10日 点击数: 字体:

主讲嘉宾: Kevin Zhou  PhD

时间:     2018年10月11日上午 9:30 – 11:00

地点:     深圳大学西丽校区A6-811

主持人:   倪东教授

主讲嘉宾简介:

Dr. S. Kevin Zhou obtained his PhD degree in EE from University of Maryland, College Park. His research interests lie in computer vision and machine learning and their applications to medical imaging AI, face recognition and modeling, etc.  Currenlty he is a Professor at Institute of Computing Technology, CAS. Previously he was a Principal Expert of Image Analysis and a Senior R&D director at Siemens Healthcare Technology. Dr. Zhou has published over 150 book chapters and peer-reviewed journal and conference papers, has registered over 250 patents and inventions, has written two research monographs, and has edited three books. His two most recent books are entitled 'Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches, SK Zhou (Ed.)' and 'Deep Learning for Medical Image Analysis, SK Zhou, H Greenspan, DG Shen (Eds.).' He has won multiple awards honoring his publications, patents and products, including Thomas Alva Edison Patent Award (2013), R&D 100 Award or Oscar of Invention (2014), Siemens Inventor of the Year (2014), and UMD ECE Distinguished Aluminum Award (2017). He has been an associate editor for IEEE Trans Medical Imaging and Medical Image Analysis journals, an area chair for CVPR and MICCAI, a co-Editor-in Chief for WeChat public journal The Vision Seeker, and elected as a fellow of American Institute of Biological and Medical Engineering (AIMBE).

报告简介(Abstract):The 'Machine learning + Knowledge modeling' approaches, which combine machine learning with domain knowledge, enable us to achieve start-of-the-art performances for many tasks of medical image recognition, segmentation and parsing. In this talk, we first present real success stories of such approaches. Then, we review the latest

about deep learning. Finally we demonstrate that the knowledge-fused deep learning approaches enable an extra performance boost.

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