报告题目：Applications of Deep Learning in Image Analyses

报告时间：2019年1月4日上午9:30

报告地点：吉大计算机大楼A521

报告人：许东 教授

报告人简介：

Dong Xu is Shumaker Endowed Professor in Department of Electrical Engineering and Computer Science, Director of Information Technology Program, with appointments in the Christopher S. Bond Life Sciences Center and the Informatics Institute at the University of Missouri-Columbia. He obtained his PhD from the University of Illinois, Urbana-Champaign in 1995 and did two years of postdoctoral work at the US National Cancer Institute. He was a Staff Scientist at Oak Ridge National Laboratory until 2003 before joining the University of Missouri, where he served as Department Chair of Computer Science during 2007-2016. His research is in computational biology and bioinformatics, including machine-learning application in bioinformatics, protein structure prediction, post-translational modification prediction, high-throughput biological data analyses, in silico studies of plants, microbes and cancers, biological information systems, and mobile App development for healthcare. He has published more than 300 papers. He was elected to the rank of American Association for the Advancement of Science (AAAS) Fellow in 2015.

报告内容简介：

We have applied deep learning in several image analysis and prediction problems, including environmental pollution assessment, tongue image analysis for health assessment, and iris recognition. These applications integrated deep learning methods, such as Convolutional Neural Network (CNN) and Capsule Network with other artificial intelligence approaches, such as fuzzy methods and attention mechanisms . Some of these applications represent novel formulations of the problems, while others significantly improved the performance over the previous methods. These studies also addressed some important deep-learning issues, such as handling small data, and making the models transparent and explainable.

主办单位：

太阳成集团tyc4633(中国)有限公司-百度百科

吉林大学软件学院

吉林大学计算机科学技术研究所

符号计算与知识工程教育部重点实验室

吉林大学国家级计算机实验教学示范中心