This talk will introduce our research on medical imaging analysis and computer-aided techniques and applications. Recently developed deep learning methods on various neuroimaging applications will be discussed from the three main aspects of neuroimage analysis, individual-based analysis and image synthisis. In order to achieve the goal of the early diagnosis and prediction of brain diseases, the following topics will be focused: the early diagnosis of infantile autism, the automatic measurement methods of the early detection of Alzheimer’s disease in the elderly, the prediction system of the survival time of brain tumor patients after operations, and how to develop machine learning and deep learning methods on medical imaging fields.
Dinggang Shen is Jeffrey Houpt Distinguished Investigator, and a Professor of Radiology, Biomedical Research Imaging Center (BRIC), Computer Science, and Biomedical Engineering in the University of North Carolina at Chapel Hill (UNC-CH). He is currently directing the Center for Image Analysis and Informatics, the Image Display, Enhancement, and Analysis (IDEA) Lab in the Department of Radiology, and also the medical image analysis core in the BRIC. He was a tenure-track assistant professor in the University of Pennsylvanian (UPenn), and a faculty member in the Johns Hopkins University. Dr. Shen’s research interests include medical image analysis, computer vision, and pattern recognition. He has published more than 1000 papers in the international journals and conference proceedings, with H-index 92. He serves as an editorial board member for eight international journals. He has also served in the Board of Directors, The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, in 2012-2015, and is General Chair for MICCAI 2019. He is Fellow of IEEE, Fellow of The American Institute for Medical and Biological Engineering (AIMBE), and also Fellow of The International Association for Pattern Recognition (IAPR).