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李硕博士报告会


发表日期:2019-06-25 作者:

李硕博士报告会

报告题目:Bring Artificial Intelligence to Medical Imaging

-          Innovative Machine Learning for Medical Data Analytics

 

 

报告人:Shuo Li 博士(University of Western Ontario 

报告时间:2019625日(周二)上午930

报告地点:安徽大学磬苑校区理工楼D318

       欢迎各位老师、同学届时前往!

报告简介:

Artificial Intelligence (AI) will significantly change the practice in medicine. AI has the potential to transform the diagnosis and treatment of diseases, which could help ensure that patients get the right treatment at the right time. This talk will share our experience in developing the innovative AI tools with newly invented machine learning tools to help daily clinical medical imaging challenges.

 

主办单位安徽大学计算机科学与技术学院

欢迎各位老师、同学届时前往!

 

  

科学技术处

2019624

 

报告人简介:

Dr. Shuo Li is an associate professor in the department of medical imaging and medical biophysics at the University of Western Ontario and scientist at Lawson Health Research Institute. Before this position he was a research scientist and project manager in General Electric (GE) Healthcare for 9 years. He founded the Digital Imaging Group of London (http://digitalimaginggroup.ca/) in 2006, which is a very dynamic and highly multiple disciplinary collaboration group. He received his Ph.D. degree in computer science from Concordia University 2006, where his PhD thesis won the doctoral prize giving to the most deserving graduating student in the faculty of engineering and computer science.  He has published over 100 publications; He is the recipient of several awards from GE, institutes and international organizations; He serves as guest editors and associate editor in several prestigious journals in the field; He serves as program committee members in highly influential conferences; He is the editors of six Springer books; He serves on the board of directors in prestigious MICCAI society. He will be the general chair for MICCAI 2022 conference. His current interest is development intelligent analytic tools to help physicians and hospital administrators to handle the big medical data, centred with medical images.

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