Machine Learning + Knowledge: Medical Image Recognition,Segmentation and Parsing

  时间:2018 年4 月23 日(周一)上午09:30~11:00

  地点:计算所446 会议室



  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.


  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).