报告题目：Machine Learning + Knowledge: Medical Image
Recognition, Segmentation and Parsing
时间：4月23日上午09:30~11:00
地点：计算所446会议室
报告摘要：
The "Machine learning + Knowledge" 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 proceed to elaborate deep learning, a special, mighty
type of machine learning method, and its use in medical imaging. We
conjecture that deep learning approaches, when fused with knowledge,
often achieve better performance than those without knowledge fusion.
报告人简介：
Dr. S. Kevin Zhou was a Principal Key Expert of Image Analysis at Siemens
Healthcare Technology, dedicated to researching and developing innovative
solutions for medical and industrial imaging products. His research interests
lie in computer vision and machine learning and their applications to medical
image recognition and parsing, face recognition and modeling, etc. 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, Segmenation 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).
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