Biomedical Image Analysis and Quantitative Bioimaging Informatics Using Big Data

Date(s) - 05/12/2014
4:15 pm

Dr. Lin Yang, Assistant Professor, Departments of Computer Science and Biostatistics, University of Kentucky


We are living in a revolutionary age, witnessing the next-generation of biomedical information emerged in astounding volumes and rich formats. Nowadays images and videos are widely used in biological research and medical applications. In this talk, I will present the recent research activities in the biomedical image computing and imaging informatics (BICI2) lab at University of Kentucky. We will start from 2D high-throughput biological and medical image analysis and large scale bioimaging informatics using big data, including developing Grid/Cloud enabled bioimaging informatics for muscle, breast tissue microarray, blood cancer, and other applications including lung cancer and neuroendocrine tumor. Then I will present the BICI2’s research activities in 3D/4D radiology image/video analysis. A prediction based collaborative tracking algorithm will be presented for robust and fast 3D heart tracking in Ultrasound, CT, and MRI. Manifold learning, non-rigid registration, and one-step forward prediction were applied to generate shape and motion priors. Machine learning based classifiers and active shape models were used to automatically detect the positions of heart chambers and segment the boundaries of heart chambers. If time permits, I will present some other research in BICI2 lab related to machine learning, such as sparse dictionary, online learning and kernelized hashing, and deep learning for fast biomedical object tracking and large scale biomedical image searching and retrieval.

Short Bio:

Dr. Lin Yang is an assistant professor in the Division of Biomedical Informatics, Department of Computer Science and Department of Biostatistics at University of Kentucky. He received his Bachelor and Master degree in Xian Jiaotong University Gifted Class in 1999 and 2002, majored in Information Technology and Signal/Image Processing, respectively. He received his Ph. D degree in the Department of Electrical and Computer Engineering in 2009 from Rutgers University with a focus on biomedical image analysis and imaging informatics. Before joining University of Kentucky in 2011, he was an assistant professor in the Department of Radiology and graduate faculty in the Department of Biomedical Engineering at Rutgers University from 2009-2011. He did part of his research in Siemens Corporate Research and IBM T. J. Watson Research Lab. He had over 15 years of research experience in biomedical image analysis, quantitative imaging informatics, and machine learning with 60 peer-reviewed journal and conference articles. His recent research also focuses on biomedical image computing and imaging informatics using high performance computing and big data. He is the winner of NIH Young Investigator Paper and Travel Award in 2008 International Symposium on Biomedical Imaging (ISBI), the winner of the Young Investigator Award in 2013 Annual North America NeuroEndocrine Tumor Society (NANETs) Conference and 2014 Annual European Neuroendocrine Tumor Society (ENETS) Conference, and the winner of the Best Student Paper Travel Award in 2013 International Conference on Medical Image Computing and Computer Aided Intervention (MICCAI). He serves as Program Committee and Chairs for more than 10 international conferences in the past three years. His research lab is actively sponsored by multiple active NIH grants.