报告题目：Medical imaging computing for COVID-19 Diagnosis

报告摘要

COVID-19 is a pandemic disease, which already caused almost 2 million death tolls till Jan/2021. This pandemic is still in ongoing spreading to the whole world. Medical imaging computing is a new interdisciplinary field that touches artificial intelligence, deep learning, medical imaging, soft computing, etc. This report will brief recent AI models (custom convolutional neural network, graph neural network, transfer learning, attention mechanism, and online cloud computing web service) to automatic diagnosis of COVID-19 based on chest CT scans. Those models implicitly capture intricate structures of large-scale chest CT images and ideally suited to some of the hardware architectures that are currently available.

嘉宾简介

Prof. Yu-Dong Zhang received the PhD degree in Signal and Information Processing from Southeast University in 2010. He worked as a postdoc from 2010 to 2012 with Columbia University, USA; and as an assistant research scientist from 2012 to 2013 with Research Foundation of Mental Hygiene (RFMH), USA. He served as a Full Professor from 2013 to 2017 with Nanjing Normal University. Now he serves as Professor with School of Informatics, University of Leicester, UK. His research interests include deep learning and medical image analysis. Prof. Zhang is the Fellow of IET (FIET), and Senior Members of IEEE, IES, and ACM. He was included in “Most Cited Chinese researchers (Computer Science)” by Elsevier from 2014 to 2018. He was the 2019 recipient of “Web of Science Highly Cited Researcher”. He won “Emerald Citation of Excellence 2017” and “MDPI Top 10 Most Cited Papers 2015”. He was included in "Top Scientist" in Guide2Research. He is the author of over 200 peer-reviewed articles, including more than 30 “ESI Highly Cited Papers”, and 3 “ESI Hot Papers”. His citation reached 13719 in Google Scholar, and 8025 in Web of Science. He has conducted many successful industrial projects and academic grants from NSFC, NIH, Royal Society, EPSRC, MRC, and British Council.

特别感谢本次Webinar主要组织者：

戴亚康（中国科学院苏州生物医学工程技术研究所）