Speaker: Dr. Le Lv (NIH Scientist)

Time and Date: 3:00 pm, November 2nd, 2017

Place: Room 521 of Physical Building, Handan Campus, Fudan University

Abstract:

The recent rapid and tremendous success of deep neural networks on many challenging computer vision tasks derives from the accessibility of the well-annotated ImageNet and PASCAL VOC datasets. Nevertheless, unsupervised image categorization (that is, without ground-truth labeling) is much less investigated, critically important, and difficult when annotations are extremely hard to obtain in the conventional way of "Google Search" + crowd sourcing (exactly how ImageNet was constructed). We'll present recent work on building two truly large-scale radiology image databases at NIH to boost the development in this important domain. The first one is a chest X-ray database of 110,000+ images from 30,000+ patients, where the image labels were obtained by sophisticated natural language processing-based text mining and the image recognition benchmarks were conducted using weakly supervised deep learning. The other database contains about 216,000 CT/MRI images with key medical findings from 61,845 unique patients, where a new looped deep pseudo-task optimization framework is proposed for joint mining of deep CNN features and image labels. Both medical image databases will be released to the public.

Biography:

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 precise novel 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 20 U.S. and international patents and is the inventor or co-inventor of 32 inventions. Le has authored over 110 peer-reviewed papers and 15 RSNA abstracts. 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, the best mentor award from NIH Clinical Center in 2013. He was the area chair or senior PC member for MICCAI 2015, 2016; CVPR 2017 and ICIP 2017, a senior member of IEEE since 2014.