Machine/Deep Learning in MRI-based AD Diagnosis
编号：115
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更新：2021-11-05 16:46:53
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特邀报告
报告开始：2021年11月14日 09:55 （Asia/Shanghai）
报告时间：25min
所在会议：[PS2] Plenary Session 2 & CT Session » [SunMS] Sunday Morning Session
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摘要
This talk will introduce our recent machine learning / deep learning work on brain quantification and disease diagnosis, using structural MRI and/or resting-state fMRI, i.e., for early diagnosis of Alzheimer’s Disease (AD). Since brain functional connectivity and the complex brain networks are very informative for investigation of cognitive function and its alterations due to various mental disorders, we have recently developed several advanced machine learning and deep learning frameworks for high-order brain network construction and also learning discriminative features from brain networks for individualized disease diagnosis. Moreover, these tools have been recently integrated into a toolbox for facilitating clinicians and neuroscientists to construct brain networks and conduct network-based analysis/classification. Details of all these methods will be introduced in this talk.
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报告人
Dinggang Shen
ShanghaiTech UniversityDinggang Shen is Professor and Founding Dean of School of Biomedical Engineering, ShanghaiTech University, and also Co-CEO of United Imaging Intelligence (UII). He is Fellow of IEEE, Fellow of The American Institute for Medical and Biological Engineering (AIMBE), Fellow of The International Association for Pattern Recognition (IAPR), and also Fellow of The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society. He was Jeffrey Houpt Distinguished Investigator, and (Tenured) Full Professor in the University of North Carolina at Chapel Hill (UNC-CH), directing The Center of Image Analysis and Informatics, The Image Display, Enhancement, and Analysis (IDEA) Lab, and The Medical Image Analysis Core. His research interests include medical image analysis, computer vision, and pattern recognition. He has published more than 1380 peer-reviewed papers in the international journals and conference proceedings, with H-index 116 and 54000+ citations. He serves as Editor-in-Chief for Frontiers in Radiology, as well as editorial board member for eight international journals. Also, he has served in the Board of Directors, The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, in 2012-2015, and was General Chair for MICCAI 2019.
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