Dr Jun Kong (a senior research scientist in the Department of Biomedical Informatics at Emory University) will give a talk titled "Analysis and Integration of Large-scale Microscopy Imaging Data for Biomedical Translational Research" on Friday, March 7, 10:10 a.m. - 11:10 a.m at Room 328 Boyd GSRC. Refreshments will be served at 9:30 a.m. in Room 409, Boyd GSRC
In biomedical research, availability of an increasing array of high-throughput and high-resolution instruments have given rise to large datasets of imaging data - such as whole-slide histology microscopy imaging. These datasets provide highly detailed views of tissue structures at the cellular level and present a strong potential to revolutionize biomedical translational research. However, traditional human-based pathology review is not feasible to obtain this wealth of imaging information due to the overwhelming data scale and unacceptable inter- and intra-observer variability. In this talk, I will describe how to efficiently process digital microscopy images for highly discriminating phenotypic information with development of Computer-Aided Diagnosis (CAD) systems and big data analytical approaches for Neuroblastoma (NB), the most common extra cranial solid cancer in childhood. Equipped with statistical machine learning techniques, these systems can automatically detect, measure, group, and classify a large scale of anatomical structures, such as cells, from microscopy images of histological specimens to support higher-level diagnosis and follow-up scientific investigations. I will also illustrate my work on brain tumor translational research with multi-scale, multi-platform in silico experiments, involving execution of image processing algorithms on microscopy image datasets, extraction and management of in-situ micro-anatomical imaging features, and correlation of heterogeneous data types and sources. For data integrative investigations, phenotypic signatures from histopathology imaging data are synergized with patient molecular profiles, and clinical outcome, exhibiting a novel avenue to better patient stratification protocols and an improved understanding of brain pathophysiology.
Jun Kong is a senior research scientist in the Department of Biomedical Informatics at Emory University. He received a Ph.D. degree in Electrical and Computer Engineering from Ohio State University in 2008. His research interests span a few key areas in bioinformatics, with special emphases on big data analytics on neuropathology microcopy images, statistical machine learning, computer-aided diagnosis, computer vision, and heterogeneous data integration and mining for oncology translational research. He has developed Computer-aided Diagnosis (CAD) systems for analyzing a large set of whole-slide microscopic imaging data with use and creation of computer vision and statistical machine learning techniques. His research work also covers large-scale translational integrative research with large-scale, multi-platform data related to human brain tumor.
***Jun Kong is a Computer Science/Cellular Biology faculty candidate.