学 术 报 告
Deep Learning in Medical Image Analysis
（医学图像分析中的深度学习方法）
报告人：Dinggang Shen (沈定刚) 教授，美国北卡罗来纳大学教堂山分校
主持人：戴亚康研究员，医学影像技术研究室
报告时间：2014年5月7日（星期三）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 deep learning for medical image analysis. Deep learning is an unsupervised method that can discover new features suitable for different applications. Although the conventional human-made filters can be used to extract certain advanced features, it is time-consuming to discover a new filter and also the extracted features may not fit a particular study under consideration. Besides, a lot of efforts need to spend on the testing and selection of different choices of human-made features, which is difficult for the researchers with limited experience to select suitable features. On the other hand, deep learning is designed to automatically discover features, from a set of given data, for each particular application. Therefore, it is able to discover new features that were never discovered by researchers before. In the past year, we started to apply deep learning for various applications in medical image analysis area, e.g., image segmentation, registration, and disease classification, all of which can be formulated as feature-matching problems and thus can be solved effectively with the learned new features by deep learning. In this talk, I will demonstrate the applications of deep learning in segmenting hippocampus, registering brain images, and identifying brain disorders from multi-modality data (in the field of neuroimaging). I will also show the results on segmenting prostate from MR images, which is important for in vivo diagnosis of prostate cancer and also the radiotherapy of prostate cancer.
报告人简历：
沈定刚教授是国际知名学者，研究成果丰硕，并已发表学术论文400余篇。他的研究方向主要集中在生物医学图像领域，其应用范围涉及到大脑、前列腺、大腿骨、骨盆及其他人体或动物器官；获得十多项美国NIH研究基金，3项中国国家自然科学基金，1项香港研究资助局基金以及多个其他基金的资助。本次报告就目前国际最新前沿热点“深度学习”方法及其在医学图像中的应用展开讨论。
欢迎大家踊跃参加！
科研管理处
2014年5月5日
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