Structured Literature Image Finder: Extracting Information from Text and Images in Biomedical Literature
Identifieur interne : 000616 ( Main/Exploration ); précédent : 000615; suivant : 000617Structured Literature Image Finder: Extracting Information from Text and Images in Biomedical Literature
Auteurs : Pedro Coelho [États-Unis] ; Amr Ahmed [États-Unis] ; Andrew Arnold [États-Unis] ; Joshua Kangas [États-Unis] ; Abdul-Saboor Sheikh [États-Unis] ; P. Xing [États-Unis] ; W. Cohen [États-Unis] ; F. Murphy [États-Unis]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2010.
Abstract
Abstract: Slif uses a combination of text-mining and image processing to extract information from figures in the biomedical literature. It also uses innovative extensions to traditional latent topic modeling to provide new ways to traverse the literature. Slif provides a publicly available searchable database (http://slif.cbi.cmu.edu). Slif originally focused on fluorescence microscopy images. We have now extended it to classify panels into more image types. We also improved the classification into subcellular classes by building a more representative training set. To get the most out of the human labeling effort, we used active learning to select images to label. We developed models that take into account the structure of the document (with panels inside figures inside papers) and the multi-modality of the information (free and annotated text, images, information from external databases). This has allowed us to provide new ways to navigate a large collection of documents.
Url:
DOI: 10.1007/978-3-642-13131-8_4
Affiliations:
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It also uses innovative extensions to traditional latent topic modeling to provide new ways to traverse the literature. Slif provides a publicly available searchable database (http://slif.cbi.cmu.edu). Slif originally focused on fluorescence microscopy images. We have now extended it to classify panels into more image types. We also improved the classification into subcellular classes by building a more representative training set. To get the most out of the human labeling effort, we used active learning to select images to label. We developed models that take into account the structure of the document (with panels inside figures inside papers) and the multi-modality of the information (free and annotated text, images, information from external databases). This has allowed us to provide new ways to navigate a large collection of documents.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Pennsylvanie</li></region><settlement><li>Pittsburgh</li></settlement><orgName><li>Université Carnegie-Mellon</li></orgName></list><tree><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Coelho, Pedro" sort="Coelho, Pedro" uniqKey="Coelho P" first="Pedro" last="Coelho">Pedro Coelho</name></region><name sortKey="Ahmed, Amr" sort="Ahmed, Amr" uniqKey="Ahmed A" first="Amr" last="Ahmed">Amr Ahmed</name><name sortKey="Ahmed, Amr" sort="Ahmed, Amr" uniqKey="Ahmed A" first="Amr" last="Ahmed">Amr Ahmed</name><name sortKey="Arnold, Andrew" sort="Arnold, Andrew" uniqKey="Arnold A" first="Andrew" last="Arnold">Andrew Arnold</name><name sortKey="Coelho, Pedro" sort="Coelho, Pedro" uniqKey="Coelho P" first="Pedro" last="Coelho">Pedro Coelho</name><name sortKey="Cohen, W" sort="Cohen, W" uniqKey="Cohen W" first="W." last="Cohen">W. Cohen</name><name sortKey="Cohen, W" sort="Cohen, W" uniqKey="Cohen W" first="W." last="Cohen">W. Cohen</name><name sortKey="Cohen, W" sort="Cohen, W" uniqKey="Cohen W" first="W." last="Cohen">W. Cohen</name><name sortKey="Kangas, Joshua" sort="Kangas, Joshua" uniqKey="Kangas J" first="Joshua" last="Kangas">Joshua Kangas</name><name sortKey="Kangas, Joshua" sort="Kangas, Joshua" uniqKey="Kangas J" first="Joshua" last="Kangas">Joshua Kangas</name><name sortKey="Murphy, F" sort="Murphy, F" uniqKey="Murphy F" first="F." last="Murphy">F. Murphy</name><name sortKey="Murphy, F" sort="Murphy, F" uniqKey="Murphy F" first="F." last="Murphy">F. Murphy</name><name sortKey="Murphy, F" sort="Murphy, F" uniqKey="Murphy F" first="F." last="Murphy">F. Murphy</name><name sortKey="Murphy, F" sort="Murphy, F" uniqKey="Murphy F" first="F." last="Murphy">F. Murphy</name><name sortKey="Murphy, F" sort="Murphy, F" uniqKey="Murphy F" first="F." last="Murphy">F. Murphy</name><name sortKey="Sheikh, Abdul Saboor" sort="Sheikh, Abdul Saboor" uniqKey="Sheikh A" first="Abdul-Saboor" last="Sheikh">Abdul-Saboor Sheikh</name><name sortKey="Xing, P" sort="Xing, P" uniqKey="Xing P" first="P." last="Xing">P. Xing</name><name sortKey="Xing, P" sort="Xing, P" uniqKey="Xing P" first="P." last="Xing">P. Xing</name><name sortKey="Xing, P" sort="Xing, P" uniqKey="Xing P" first="P." last="Xing">P. Xing</name><name sortKey="Xing, P" sort="Xing, P" uniqKey="Xing P" first="P." last="Xing">P. Xing</name><name sortKey="Xing, P" sort="Xing, P" uniqKey="Xing P" first="P." last="Xing">P. Xing</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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