报告名称：Deep Learning for Medical Image Analysis
This talk will discuss some of our recently developed deep learning methods for various neuroimaging applications. Specifically, 1) in neuroimaging analysis, we have developed an automatic brain measurement method for the first-year brain images with the goal of early detection of autism such as before 1-year-old. This effort is aligned with our recently awarded Baby Connectome Project (BCP) (where I serve as Co-PI), which will acquire MR images and behavioral assessments from typically developing children, from birth to five years of age. Besides, we have also developed a novel landmark-based deep learning method for early diagnosis of Alzheimer’s Disease (AD) with the goal of potential early treatment. 2) In image synthesis, we have developed a cascaded 3D CNN for reconstructing 7T-like MRI from 3T MRI for simultaneously enhancing image quality and tissue segmentation. Also, we have developed a novel Generative Adversarial Networks (GAN) based technique to estimate CT from MRI, for helping MRI-based cancer radiotherapy. All these techniques will be introduced in this talk, for the goal of early diagnosis of brain disorders.
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 800 papers in the international journals and conference proceedings. 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. He will be General Chair for MICCAI 2019. He is Fellow of IEEE, Fellow of The American Institute for Medical and Biological Engineering (AIMBE), and Fellow of The International Association for Pattern Recognition (IAPR).