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10.4103/2153-3539.159214 http://scihub22266oqcxt.onion/10.4103/2153-3539.159214 C4485195!4485195!26167381     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Pathol+Inform 2015 ; 6 (ä): ä Nephropedia Template TP {{tp|p=26167381|t=2015. Biomedical imaging ontologies: A survey and proposal for future work |pdf=|usr=}}{{26167381}} gab.com Text Biomedical imaging ontologies: A survey and proposal for future work JO: J Pathol Inform http://www.kidney.de/pget.php?&tw=1&pmid=26167381 #FOAvip Background:: Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology is a controlled structured vocabulary consisting of general terms (such as ?cell? or ?image? or ?tissue? or ?microscope?) that form the basis for such tagging. These terms are designed to represent the types of entities in the domain of reality that the ontology has been devised to capture; the terms are provided with logical definitions thereby also supporting reasoning over the tagged data. Aim:: This paper provides a survey of the biomedical imaging ontologies that have been developed thus far. It outlines the challenges, particularly faced by ontologies in the fields of histopathological imaging and image analysis, and suggests a strategy for addressing these challenges in the example domain of quantitative histopathology imaging. Results and Conclusions:: The ultimate goal is to support the multiscale understanding of disease that comes from using interoperable ontologies to integrate imaging data with clinical and genomics data. Twit Text FOAVip Biomedical imaging ontologies: A survey and proposal for future work ????J Pathol Inform http://www.kidney.de/pget.php?&tw=1&pmid=26167381 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26167381 #FOAvip English Wikipedia <ref>{{Cite pmid|26167381}}</ref> Biomedical imaging ontologies: A survey and proposal for future work #MMPMID26167381 Smith B; Arabandi S; Brochhausen M; Calhoun M; Ciccarese P; Doyle S; Gibaud B; Goldberg I; Kahn CE; Overton J; Tomaszewski J; Gurcan M J Pathol Inform 2015[]; 6 (ä): ä PMID26167381show ga Background:: Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology is a controlled structured vocabulary consisting of general terms (such as ?cell? or ?image? or ?tissue? or ?microscope?) that form the basis for such tagging. These terms are designed to represent the types of entities in the domain of reality that the ontology has been devised to capture; the terms are provided with logical definitions thereby also supporting reasoning over the tagged data. Aim:: This paper provides a survey of the biomedical imaging ontologies that have been developed thus far. It outlines the challenges, particularly faced by ontologies in the fields of histopathological imaging and image analysis, and suggests a strategy for addressing these challenges in the example domain of quantitative histopathology imaging. Results and Conclusions:: The ultimate goal is to support the multiscale understanding of disease that comes from using interoperable ontologies to integrate imaging data with clinical and genomics data. äBiomedical imaging ontologies: A survey and proposal for future work (epmc).pdf DeepDyve Pubget Overpricing