Applications of MRI-based Deep Learning Models to Prostate Cancer Care
Deep learning (DL) has recently garnered significant attention because of its superior performance in image recognition and classification. One of the central promises of DL is to replace handcrafted imaging features with efficient algorithms for hierarchical feature extraction. We present recently developed magnetic resonance imaging (MRI)-driven deep learning models for improved segmentation, detection, and classification of clinically significant prostatic carcinoma. The presentation will mainly highlight i) unique challenges of MRI data in DL frameworks, ii) construction of DL frameworks through convolutional neural networks (CNNs), and iii) application of the DL framework to the computerized analysis of prostate MRI for improved cancer care.
Dr. Sung is an Associate Professor of Radiology, where his research primarily focuses on the development of novel medical imaging methods and artificial intelligence using magnetic resonance imaging (MRI). He received a Ph.D. degree in Electrical Engineering from the University of Southern California, Los Angeles, in 2008, and from 2008 to 2012, he finished his postdoctoral training at Stanford in the Departments of Radiology. He joined the University of California, Los Angeles (UCLA) Department of Radiological Sciences, in 2012. His research interest is to develop fast and reliable magnetic resonance imaging (MRI) techniques that can provide improved diagnostic contrast and useful information. In particular, his research group (https://mrrl.ucla.edu/sunglab/) is currently focused on developing advanced deep learning algorithms and quantitative MRI techniques for early diagnosis, treatment guidance, and therapeutic response assessment for oncologic applications. Such developments can offer more robust and reproducible measures of biologic markers associated with human cancers.