Quantitative MRI-Driven Deep Learning
Deep Learning (DL) has recently garnered great attention because of its superior performance in image recognition and classification. One of the main promises of DL is to replace handcrafted imaging features with efficient algorithms for hierarchical feature extraction. Many studies have shown DL is a powerful engine for producing “actionable results” in unstructured big data. We present deep learning methods to effectively distinguish between indolent and clinically significant prostatic carcinoma using multi-parametric MRI (mp-MRI). The main contributions include i) constructing DL frameworks to avoid massive learning requirements through pre-trained convolutional neural network (CNN) models and ii) applying the proposed DL framework to the computerized analysis of prostate multi-parametric MRI from improved cancer classification.
Dr. Sung received the M.S and Ph.D. degrees in Electrical Engineering from University of Southern California, Los Angeles, in 2005 and 2008, respectively. From 2008 to 2012, he finished his postdoctoral training at Stanford in the Departments of Radiology and joined the University of California, Los Angeles (UCLA) Department of Radiological Sciences in 2012 as an Assistant Professor. His research interest is to develop fast and reliable MRI methods that can provide improved diagnostic contrast and useful information. In particular, his group (http://mrrl.ucla.edu/meet-our-team/sung-lab/) is currently focused on developing advanced quantitative MRI techniques for early diagnosis, treatment guidance, and therapeutic response assessment for oncologic and cardiac applications