主持人：戴亚康研究员，医学影像技术研究室
报告时间：2015年7月9日（星期四）上午10:00-11:30
报告地点：A3行政综合楼312会议室
报告摘要：
This talk will summarize our recent work on using novel machine learning techniques to predict or synthesize one modality image from other modality image(s). The typical applications, i.e., in PET/MRI scanner, include 1) predicting high-quality PET from both low-quality PET and MRI to reduce the tracer dose without degrading the PET image quality, and 2) predicting CT from MRI for attenuation correction of the acquired PET image. We have developed various sparse learning techniques to establish the internal relationships among different modality images and then use them for prediction or synthesis of target image. We have also used the structured random forest and auto-context model to learn the deep architecture for mapping source image(s) (i.e., MRI) to the target image (i.e., CT). All these techniques and their respective applications will be discussed in this talk.
报告人简历：
Dr. Dinggang Shen is a tenured professor of Radiology, Biomedical Research Imaging Center (BRIC), Computer Science and Biomedical Engineering in the University of North Carolina at Chapel Hill (UNC). He is currently directing the Center for Image 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 received his bachelor, master, and doctoral degrees from the Department of Electronic Engineering, Shanghai Jiao Tong University in 1990, 1992, and 1995 respectively. He was a faculty member in the Johns Hopkins University and a tenure-track assistance professor in the University of Pennsylvania. Dr. Shen’s research interest includes medical image analysis, computer vision, and pattern recognition. He has published more than 500 papers in the international journals and conference proceedings. He serves as an editorial board member for seven international journals. He also serves in the Board of Directors, The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society.
欢迎大家踊跃参加！
科研管理处
2015年7月8日
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