报告时间：2016年7月1日（周五）
上午 10:30-11:30
报告地点：计算所 446室
主讲人：陈子仪 教授
University of Notre Dame
报告摘要：
Computer technology plays a vital role in modern medicine, health care, and life sciences. Computational research and applications on modeling, formulating, and solving core problems in medicine and health care are not only crucially needed, but are actually indispensable.
Deep learning (DL) techniques have achieved remarkable performance for many computer vision tasks (e.g., image classification, object detection, and semantic segmentation). In this talk, we present new approaches based on DL techniques for solving a number of medical problems: identifying and classify immune cells in H&E staining histology images for diagnosis and treatment response monitoring of inflammation diseases (e.g., rheumatoid arthritis and inflammatory bowel disease) based on CNN and FCN, detecting and analyzing glial cells interacting with tumors in the brain microenvironment of metastatic breast cancer in 3D microscopy images based on U-Net, identifying and classifying glands and villi in histology colon images for diagnosis of inflammatory bowel disease based on CNN, tracking bacteria motion in time-lapse images based on RNN, and segmenting and analyzing fungus cells that collectively control their host's behaviors based on U-Net. The image sizes in our medical applications tend to be very large. For example, even one 2D H&E histology image is of size 30000 * 30000 (for diagnosing inflammation diseases); one 3D whole brain image has over 1011 voxels (for studying brain metastasis of breast cancer).
主讲人简介：
Dr. Danny ZiyiChenreceived the B.S. degrees in Computer Science and in Mathematics from the University of San Francisco, California, USA in 1985, and the M.S. and Ph.D. degrees in Computer Science from Purdue University, West Lafayette, Indiana, USA in 1988 and 1992, respectively. He has been on the faculty of the Department of Computer Science and Engineering at the University of Notre Dame, Indiana, USA since 1992, and is currently a Professor. Dr. Chen's main research interests are in computational biomedicine, biomedical imaging, computational geometry, algorithms and data structures, data mining, and VLSI. He has published many journal and conference papers in these areas, and holds 5 US patents for technology development in computer science and engineering and biomedical applications. Dr. Chen is a Fellow of IEEE, and a Distinguished Scientist of ACM. He received the CAREER Award of the US National Science Foundation (NSF) in 1996, the James A. Burns, C.S.C. Award for Graduate Education of the University of Notre Dame in 2009, and a Laureate Award in the 2011 Computerworld Honors Program for "Arc-Modulated Radiation Therapy" for developing a new radiation cancer treatment
approach.
附件下载：
Computational Medicine with Deep Learning
上午 10:30-11:30
报告地点：计算所 446室
主讲人：陈子仪 教授
University of Notre Dame
报告摘要：
Computer technology plays a vital role in modern medicine, health care, and life sciences. Computational research and applications on modeling, formulating, and solving core problems in medicine and health care are not only crucially needed, but are actually indispensable.
Deep learning (DL) techniques have achieved remarkable performance for many computer vision tasks (e.g., image classification, object detection, and semantic segmentation). In this talk, we present new approaches based on DL techniques for solving a number of medical problems: identifying and classify immune cells in H&E staining histology images for diagnosis and treatment response monitoring of inflammation diseases (e.g., rheumatoid arthritis and inflammatory bowel disease) based on CNN and FCN, detecting and analyzing glial cells interacting with tumors in the brain microenvironment of metastatic breast cancer in 3D microscopy images based on U-Net, identifying and classifying glands and villi in histology colon images for diagnosis of inflammatory bowel disease based on CNN, tracking bacteria motion in time-lapse images based on RNN, and segmenting and analyzing fungus cells that collectively control their host's behaviors based on U-Net. The image sizes in our medical applications tend to be very large. For example, even one 2D H&E histology image is of size 30000 * 30000 (for diagnosing inflammation diseases); one 3D whole brain image has over 1011 voxels (for studying brain metastasis of breast cancer).
主讲人简介：
Dr. Danny ZiyiChenreceived the B.S. degrees in Computer Science and in Mathematics from the University of San Francisco, California, USA in 1985, and the M.S. and Ph.D. degrees in Computer Science from Purdue University, West Lafayette, Indiana, USA in 1988 and 1992, respectively. He has been on the faculty of the Department of Computer Science and Engineering at the University of Notre Dame, Indiana, USA since 1992, and is currently a Professor. Dr. Chen's main research interests are in computational biomedicine, biomedical imaging, computational geometry, algorithms and data structures, data mining, and VLSI. He has published many journal and conference papers in these areas, and holds 5 US patents for technology development in computer science and engineering and biomedical applications. Dr. Chen is a Fellow of IEEE, and a Distinguished Scientist of ACM. He received the CAREER Award of the US National Science Foundation (NSF) in 1996, the James A. Burns, C.S.C. Award for Graduate Education of the University of Notre Dame in 2009, and a Laureate Award in the 2011 Computerworld Honors Program for "Arc-Modulated Radiation Therapy" for developing a new radiation cancer treatment
approach.