Seminar Series - Fuyong Xing - High-throughput Biomedical Image Computing for Digital Health
In biomedical informatics, a large amount of image data has been collected to support clinical diagnosis, treatment decision and medical prognosis. The large volume and the diversity of informatics across different imaging modalities require advanced and high-throughput image computing technologies for more accurate disease detection, deeper understanding of the mechanisms of disease progression, and better healthcare in precision medicine. With the ever- increasing amount of biomedical image data, it is very important to design and develop efficient technologies for large-scale biomedical image analysis. This talk will describe high-throughput biomedical image computing methods for digital health, focusing on three significant topics: object detection, segmentation, and image understanding in medical diagnosis. Specifically, I will present several novel machine learning and imaging informatics technologies to process biomedical big image data and introduce the applications of these technologies in medical diagnosis
Fuyong Xing is a Ph.D. Candidate in the Department of Electrical and Computer Engineering at University of Florida. He received his M.S. from Rutgers, The State University of New Jersey in 2011 and bachelor’s degree from Xi'an Jiaotong University in 2006. His research interests include medical image computing, biomedical imaging informatics, and machine learning. He has published over 30 peer-reviewed journal and conference proceedings. He was the winner of Outstanding International Student Awards at University of Florida in 2016 and the Runners Up for Young Scientist Awards in the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015.