We propose to advance biomedical literature mining by providing new technologies for searching and retrieving biomedical images. The content of these images is a good representation of the research results discussed in scientific articles, and often contains additional facts not explicitly mentioned in the article or image caption. While the inherent structure in biomedical images facilitates automated image content extraction, the extraction process can be made more accurate by concurrent processing of text and images. Text-enhanced image analysis results in richly annotated images, which open up new possibilities for locating images of interest.
The specific aims are to 1) develop methods for extracting structured image content through image processing and analysis, to 2) design methods to boost accuracy of image understanding through concurrent processing of text and images, to 3) devise methods for searching across structured image content, and 4) to develop an image search tool that demonstrates the power of using structured Image content for accessing the biomedical literature.
We propose to advance our ability to search Millions of published research articles by looking into the images within those articles. Our methodology understands the text and layout within images, allowing for very precise image searches. The methodology should have a broad impact on our ability to access the biological and medical literature.
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