Deep Neural Networks in Medical Imaging and Radiology

A Google TechTalk, 5/11/17, presented by Le Lu ABSTRACT: Deep Neural Networks in Medical Imaging and Radiology: Preventative and Precision Medicine Perspectives (the final version from GTC, GTCx, GTC-DC 2016) Employing deep learning (DL), especially deep neural networks for high performance radiological or medical image computing is the main focus. We will present the motivation, technical details and quantitative results of our recent work at NIH for three core problems: 1) Improving Computer-aided Detection (CAD) using Convolutional Neural Networks and Decompositional Image Representations; 2) Robust Bottom-up Multi-level Deep Convolutional Networks for Automated Organ Segmentation; 3) Text/Image Deep Mining on a Large-Scale Radiology Image Database for Automated Image Interpretation. We validate some very promising observations of using DL to both significantly improve upon traditional CAD tasks in (1) and enable new exciting research directions as (2,3). Insights and future directions will be discussed. Speaker Info: Dr. Le Lu Le Lu has served as a staff scientist since 2013 in the Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Maryland. His research is focused on medical image understanding and semantic parsing to fit into new clinical practices, especially in the areas of preventive early cancer detection/diagnosis and developing novel precision imaging bio-markers, via large-scale imaging protocols and statistical (deep) learning principles. Le worked on various core R&D problems in colonic polyp and lung nodule CADx systems, and vessel/bone imaging at Siemens Corporate Research and Siemens Healthcare from 2006 to 2013, and his last post was a senior staff scientist. He has been named on 18 U.S. and international patents and is the inventor or co-inventor of 32 inventions. Le has authored over 100 peer-reviewed papers. He received his Ph.D. in computer science from Johns Hopkins University in 2007. He won the NIH Mentor of the Year award in the staff scientist/clinician category in 2015. He was an Area Chair for IEEE CVPR 2017 and ICIP 2017, and a senior program committee member (equivalent to Area Chair) for MICCAI 2016 and 2015.