报告时间：2月28日 15:00-16:30

报告地点：屏峰校区A1区块计算机大楼D305

报告题目：

Deep Learning in Quantitative Organ Measurement and Disease Treatment

报告人：

Dinggang Shen ,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 900 papers in the international journals and conference proceedings, with H-index 85. 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 is General Chair for MICCAI 2019 in Shenzhen, China. 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).

内容摘要：

This talk will introduce our recent deep learning work on quantification of brain development, aging, and disorders, as well as cancer treatment. Specifically, for automatic quantification of early brain development in the first year of life, i.e., with the goal of early identification of brain diseases such as autism, deep learning based brain image segmentation and cortical surface parcellation will be introduced in this talk. Also, for early diagnosis of Alzheimer’s Disease (AD) with the goal of possible early treatment, deep learning is applied to unsupervised brain registration for precise inter-subject comparison and distinctive-regions based disease diagnosis. Besides, for effective treatment of prostate cancer, especially for MRI-based cancer treatment, a novel context-aware GAN (Generative Adversarial Networks) has been developed for synthesizing CT from MRI. To better guide radiotherapy, a novel deep learning technique has also been developed for automatic and precise segmentation of pelvic organs from the planning CT images. In this talk, the clinical significance of each of these medical problems, as well as our main idea for each developed technique, will be introduced.