报告时间：12月13日（星期二）上午10：30-12：00
报告地点：浙大紫金港东1A-505 (多媒体教室)
报告题目：Deep Learning Approaches for Computational Medicine and Health Care Problems
报告人：Prof. Danny Chen
主持人：陈为教授

Abstract:Computer technology plays a vital role in modern medicine, health care, and life sciences, especially in medical imaging, human genome study, clinical diagnosis and prognosis, treatment planning and optimization, and medical data management and analysis.  As computing technology continues to evolve, computer science research and development will inevitably become an integral part of modern medicine and health care. 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, such as identifying and classify immune cells in H&E stained histology tissue 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, segmenting and analyzing fungus cells that collectively control their host's behaviors based on U-Net, etc. The image sizes in our medical applications tend to be very large. We show that simply applying DL techniques alone is often insufficient to solve our medical problems. Hence, we construct new methods to complement and work with DL techniques. For example, we devise a new geometric “context" model based on the Voronoi diagram of clusters to capture cell context information, which complements the object identification capability of CNN, for identifying immune cells. We combine CNN with a process of computing maximal independent set to identify glands and villi in colon images. We incorporate U-Net with the Earth Mover's Distance based matching model to segment fungus cells in 3D images for building fungi interaction networks. A key point is that DL is used as one main step in our approaches, which is complemented by other major steps. Further, we carefully select and combine parts (e.g., layers) of DL networks for specific applications. For example, we develop a multi-scale FCN based approach for identifying medical regions with vastly different sizes and shapes, by reorganizing a set of FCNs of different scales and combine layers of FCNs to form new DL networks. We show experimental data and results to illustrate the clinical applications of our approaches.

Bio:Dr. Danny Ziyi Chen （陈子仪）received 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.