报告主题：大规模医学影像数据库真实构建：深度标签发现与开放式识别
报告人：Le Lu 研究员 （National Institutes of Health）
报告时间：2017年 11月3日（周五）10:00
报告地点：校本部G507
邀请人：应时辉
主办部门：8455新葡萄场网站数学系
报告摘要：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.
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