Prof. Yudong Zhang worked as a postdoc from 2010 to 2012 with Columbia University, USA, and as an Assistant Research Scientist from 2012 to 2013 with the 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 a professor at the School of Computing and Mathematical Sciences, University of Leicester, UK. His research interests include deep learning and medical image analysis.
He is the Fellow of IET, Fellow of EAI, and Fellow of BCS. He is the Senior Member of IEEE, IES, and ACM. He is the Distinguished Speaker of ACM. He was included in Most Cited Chinese Researchers (Computer Science) by Elsevier from 2014 to 2018. He was the 2019 & 2021 recipient of Highly Cited Researcher by Clarivate. He won the Emerald Citation of Excellence 2017 and MDPI Top 10 Most Cited Papers 2015. He is included in Top Scientist in Research.com. He has (co)authored over 400 peer-reviewed articles in the journals JAMA Psychiatry, Inf Fus, IEEE TFS, IEEE TII, IEEE TIP, IEEE TMI, IEEE IoTJ, Neural Networks, IEEE TITS, Pattern Recognition, IEEE TGRS, IEEE JBHI, IEEE TCSVT, IEEE TETCI, IEEE TCSS, IEEE JSTARS, IEEE TNSRE, IEEE Sensors J, ACM TKDD, ACM TOMM, IEEE/ACM TCBB, IEEE TCAS-II, IEEE JTEHM, ACM TMIS, etc. There are more than 50 ESI Highly Cited Papers and 5 ESI Hot Papers in his (co)authored publications.
His citation reached 21143 in Google Scholar (h-index 81) and 12418 in Web of Science (h-index 61). He has conducted many successful industrial projects and academic grants from NIH, Royal Society, GCRF, EPSRC, MRC, Hope, British Council, and NSFC. He has given over 120 invited talks at international conferences, universities, and companies. He has served as (Co-)Chair for more than 60 international conferences and workshops. His research outputs have been reported by more than 50 news press, such as Reuters, BBC, Telegraph, Physics World, UK Today News, EurekAlert! Science News, India Times, Association of Optometrists (AOP) news, Medical Xpress, HospiMedica, Newsroom Odisha, etc.
Speech title: Information Engineering Theories and Methods for COVID-19 Diagnosis
Abstract: COVID-19 is a pandemic disease that caused more than 6.67 million deaths until 26/Dec/2022. X-ray and CT scans are two popular medical imaging technique used in radiology to get detailed images of the body noninvasively for diagnostic purposes. Traditional manual labeling of X-ray or CT-based scans is tedious and error-prone. To solve the problem, our lab develops and applies new information engineering theories and methods, such as advanced pooling-based networks, graph convolutional networks, attention neural networks, weakly-supervised networks, etc. We also use cloud computing techniques to run our developed app on the remote server to help doctors in the suburban area. Two other chest-related diseases: secondary pulmonary tuberculosis and community-acquired pneumonia, will be covered in this talk.