During this educational session, participants will learn about the potential impact of artificial intelligence (AI) and machine learning (ML) on radiology informatics and how large institutions are exploring new ways to use AI and ML techniques in research to develop new predictive tools within neuroimaging. In this session, we will explore some of the research that is currently underway and how artificial intelligence and machine learning are changing the way that researchers study aging and specific disease areas such as Alzheimer's.
The presenters will:
Monday, November 27, 2017
Li Shen, PhD
Associate Professor, Center for Neuroimaging, Department of Radiology and Imaging Sciences
Associate Director, Center for Computational Biology and Bioinformatics
Indiana University School of Medicine
Li Shen, PhD, is an associate professor at Indiana University. His research includes medical image computing, bioinformatics, machine learning, network science, visual analytics, and big data science in biomedicine. His laboratory is focused on developing computational and informatics methods for integrative analysis of multimodal imaging data, high-throughput "omics" data, cognitive and other biomarker data, electronic health record data, and rich biological knowledge such as pathways and networks, with applications to various complex disorders. The laboratory’s goal is twofold: (1) advance computer science and bioinformatics by producing novel algorithms for analyzing large-scale heterogeneous datasets; and (2) provide important new insights into the phenotypic characteristics and genetic mechanisms of normal and/or disordered biological structures and functions to impact the development of new diagnostic, therapeutic, and preventive approaches. His research is primarily supported by NIH (NIBIB, NLM, NIA, NCATS, NIAAA), NSF, DOD, and NCAA.
Chair: Bill Lacy
Vice President, Medical Informatics Business
FUJIFILM Medical Systems U.S.A., Inc.
Mr. Lacy is vice president of Fujifilm’s medical informatics business unit in the U.S.
Thank you for your interest in our event! Pre-registration is now closed.
Please stop by booth #4111, South Hall for more information.