针对神经影像数据的深度学习方法
Deep Learning for Neuroimaging Analysis
Ding-gang Shen (沈定刚) 教授
美国北卡罗来纳大学教堂山分校
University of North Carolina at Chapel Hill
沈定刚教授主要从事医学图像分析、计算机视觉与模式识别等领域的研究。他提出了知名的大脑弹性配准算法HAMMER。2006年获得IEEE Signal Processing Society年度最佳论文，被引用达700多次，相关软件下载量到10000多次。已在Annual Review of Biomedical Engineering, Human Brain Mapping, NeuroImage, IEEE Trans. on Pattern Analysis & Machine Intelligence等国际学术期刊和会议上发表800多篇论文。并担任六个国际期刊的编委，是医学图像计算和计算机辅助治疗组织(MICCAI)的执委会成员和美国医学与生物工程院(AIMBE)会士。
讲座摘要
His 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), 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 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.
时间：2017年11月16日 （周四）上午10-12点
地点：北京师范大学京师学堂大楼第三会议室（地下室一层）
