Developing the Quantitative Histopathology Image Ontology : A case study using the hot spot detection problem

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Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster organized, cross-disciplinary, information-driven collaborations in the pathological imaging field, we propose to develop an ontology to represent imaging data and methods used in pathological imaging and analysis, and call it Quantitative Histopathological Imaging Ontology – QHIO. We apply QHIO to breast cancer hot-spot detection with the goal of enhancing reliability of detection by promoting the sharing of data between image analysts.
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First archival date: 2017-04-28
Latest version: 2 (2017-11-13)
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The OBO Foundry: Coordinated Evolution of Ontologies to Support Biomedical Data Integration.Smith, Barry; Ashburner, Michael; Rosse, Cornelius; Bard, Jonathan; Bug, William; Ceusters, Werner; Goldberg, Louis J.; Eilbeck, Karen; Ireland, Amelia; Mungall, Christopher J.; Leontis, Neocles & Others,
MIREOT: The Minimum Information to Reference an External Ontology Term.Courtot, Mélanie; Gibson, Frank; Lister, Allyson L.; Malone, James; Schober, Daniel; Brinkman, Ryan R. & Ruttenberg, Alan
Biomedical Imaging Ontologies: A Survey and Proposal for Future Work.Smith, Barry; Arabandi, Sivaram; Brochhausen, Mathias; Calhoun, Michael; Ciccarese, Paolo; Doyle, Scott; Gibaud, Bernard; Goldberg, Ilya; Kahn Jr, Charles E.; Overton, James; Tomaszewski, John & Gurcan, Metin

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