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2017 GTC San Jose

S7595 - Building Truly Large-Scale Medical Image Databases: Deep Label Discovery and Open-Ended Recognition

Session Speakers
Session Description

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


Additional Session Information
Intermediate
Talk
AI in Healthcare Summit Computer Vision and Machine Vision Deep Learning and AI Healthcare and Life Sciences Medical Imaging
Government / National Labs Healthcare & Life Sciences Higher Education / Research
50 minutes
Session Schedule