題目：Intelligent Computing, Big Data, and Modern Medicine and Healthcare
講者：Danny Ziyi Chen教授，IEEE Fellow，美國圣母大學
講者介紹：Danny Ziyi Chen（陳子儀）博士1985年獲得美國舊金山大學計算機科學和數學學士學位，并分別于1988年和1992年獲得美國普渡大學西拉法葉分校的計算機科學碩士和博士學位，他自1992年以來一直在美國圣母大學計算機科學與工程系任教，現任教授。陳教授的主要研究興趣是計算生物醫學，生物醫學成像，計算幾何，算法和數據結構，機器學習，數據挖掘和VLSI。他在這些領域發表了130多篇期刊論文和210多篇經過同行評審的會議論文，并擁有5項美國計算機科學與工程和生物醫學應用技術開發專利。他于1996年獲得NSF CAREER獎，2011年獲得計算機世界榮譽計劃的榮譽獎，用于開發“弧度調制放射治療”（一種新的放射性癌癥治療方法）及2017年獲美國國家科學院的PNAS Cozzarelli獎。他是IEEE Fellow和ACM杰出科學家。
講座簡介：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.