题目：Intelligent Computing, Big Data, and Modern Medicine and Healthcare
讲者：Danny Ziyi Chen教授，IEEE Fellow，美国圣母大学
讲者介绍：Danny Ziyi Chen（陈子仪）博士1985年获得美国旧金山大学计算机科学和数学学士学位，并分别于1988年和1992年获得美国普渡大学西拉法叶分校的计算机科学硕士和博士学位，他自1992年以来一直在美国圣母大学计算机科学与工程系任教，现任教授。陈教授的主要研究兴趣是计算生物医学，生物医学成像，计算几何，算法和数据结构，机器学习，数据挖掘和VLSI。他在这些领域发表了130多篇期刊论文和210多篇经过同行评审的会议论文，并拥有5项美国计算机科学与工程和生物医学应用技术开发专利。他于1996年获得NSF CAREER奖，2011年获得计算机世界荣誉计划的荣誉奖，用于开发“弧度调制放射治疗”（一种新的放射性癌症治疗方法）及2017年获美国国家科学院的PNAS Cozzarelli奖。他是IEEE Fellow和ACM杰出科学家。
讲座简介：Computer technology plays a crucial role in modern medicine, healthcare, and life sciences, especially in medical imaging, human genome study, clinical diagnosis and prognosis, treatment planning and optimization, treatment response evaluation and monitoring, and medical data management and analysis. As computer technology rapidly evolves, computer science solutions will inevitably become an integral part of modern medicine and healthcare. Computational research and applications on modeling, formulating, solving, and analyzing core problems in medicine and healthcare are not only critical, but are actually indispensable!
Recently emerging deep learning (DL) techniques have achieved remarkably high quality results for many computer vision tasks, such as image classification, object detection, and semantic segmentation, largely outperforming traditional image processing methods. In this talk, we first discuss some development trends in the area of intelligent medicine and healthcare. We then present new approaches based on DL techniques for solving a set of medical imaging problems, such as segmentation and analysis of glial cells, analysis of the relations between glial cells and brain tumors, segmentation of neuron cells, and new training strategies for deep learning using sparsely annotated medical image data. We develop new deep learning models, based on fully convolutional networks (FCN), recurrent neural networks (RNN), and active learning, to effectively tackle the target medical imaging problems. For example, we combine FCN and RNN for 3D biomedical image segmentation; we propose a new complete bipartite network model for neuron cell segmentation. Further, we show that simply applying DL techniques alone is often insufficient to solve medical imaging problems. Hence, we construct other new methods to complement and work with DL techniques. For example, we devise a new cell cutting method based on k-terminal cut in geometric graphs, which complements the voxel-level segmentation of FCN to produce object-level segmentation of 3D glial cells. We show how to combine a set of FCNs with an approximation algorithm for the maximum k-set cover problem to form a new training strategy that takes significantly less annotation data. A key point we make is that DL is often used as one main step in our approaches, which is complemented by other main steps. We also show experimental data and results to illustrate the practical applications of our new DL approaches.