Lecture 1：Machine Learning in Internet-based Intelligent Medicine
Lecture 2：Biomedical Research Imaging Center and a Brief Overview of the Baby Connectome Project
Time：13:30-15:00, November 10, 2016
Place：C708, New Main Building
This talk will describe how the future internet-based intelligent medicine will look like, as well as main challenges such as how to integrate different scale information together for helping disease diagnosis. To achieve this goal, we have recently developed various machine learning techniques, including sparse learning and deep learning, for respective applications. Specifically,1) in neuroimaging field, 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 multivariate classification method for early diagnosis of Alzheimer’s Disease (AD) with the goal of potential early treatment, as well as prediction of success of neurosurgery by collaboration with Huashan Hospital in Shanghai.2) In image reconstruction field, we have developed a sparse learning method for reconstructing 7T-like MRI from 3T MRI for enhancing image quality, and also another novel sparse learning technique for estimation of standard-dose PET image from low-dose PET and MRI data.3) Finally, in cancer radiotherapy field, we have developed an innovative regression-guided deformable model to automatically segment pelvic organs from single planning CT which is currently done manually, as well as a novel image synthesis technique for estimating CT from MRI for current new direction of MRI-based dose planning (and also for PET attenuation correction in the case of using PET/MRI scanner). All these techniques are the important components of future internet-based intelligent medicine, and will be discussed in this talk.
Dinggang Shen is 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 700 papers in the international journals and conference proceedings. He serves as an editorial board member for six international journals. He has also served in the Board of Directors, The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, in 2011-2015.