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Shangri–La: A medical case–based retrieval tool

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dc.contributor.authorGarcía Seco de Herrera, Alba
dc.contributor.authorSchaer, Roger
dc.contributor.authorMüller, Henning
dc.date.accessioned2025-03-27T07:26:07Z
dc.date.available2025-03-27T07:26:07Z
dc.date.issued2018-11-28
dc.descriptionEsta es la versión aceptada del artículo. La versión registrada fue publicada por primera vez en Journal of the Association for Information Science and Technology 68, n.º 11 (2017): 2587-2601, está disponible en línea en el sitio web del editor: Wiley, https://doi.org/10.1002/asi.23858. This is the accepted version of the article. The registered version was first published in Journal of the Association for Information Science and Technology 68, no. 11 (2017): 2587-2601, is available online at the publisher's website: Wiley, https://doi.org/10.1002/asi.23858.
dc.description.abstractLarge amounts of medical visual data are produced in hospitals daily and made available continuously via publications in the scientific literature, representing the medical knowledge. However, it is not always easy to find the desired information and in clinical routine the time to fulfil an information need is often very limited. Information retrieval systems are a useful tool to provide access to these documents/images in the biomedical literature related to information needs of medical professionals. Shangri–La is a medical retrieval system that can potentially help clinicians to make decisions on difficult cases. It retrieves articles from the biomedical literature when querying a case description and attached images. The system is based on a multimodal retrieval approach with a focus on the integration of visual information connected to text. The approach includes a query–adaptive multimodal fusion criterion that analyses if visual features are suitable to be fused with text for the retrieval. Furthermore, image modality information is integrated in the retrieval step. The approach is evaluated using the ImageCLEFmed 2013 medical retrieval benchmark and can thus be compared to other approaches. Results show that the final approach outperforms the best multimodal approach submitted to ImageCLEFmed 2013.en
dc.description.versionversión final
dc.identifier.citationAlba García Seco de Herrera, Roger Schaer, y Henning Müller. «Shangri–La: A medical case–based retrieval tool». Journal of the Association for Information Science and Technology 68, n.º 11 (2017): 2587-2601. https://doi.org/10.1002/ASI.23858.
dc.identifier.doihttps://doi.org/10.1002/asi.23858
dc.identifier.issn2330-1635; e-ISSN: 2330-1643
dc.identifier.urihttps://hdl.handle.net/20.500.14468/26369
dc.journal.issue11
dc.journal.titleAssociation for Information Science and Technology
dc.journal.volume68
dc.language.isoen
dc.page.final2601
dc.page.initial2587
dc.publisherWiley
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.subject.keywordsMedical visual information retrievalen
dc.subject.keywordsImageCLEFen
dc.subject.keywordsMedical case retrievalen
dc.subject.keywordsQuery adaptive multi–modal fusionen
dc.subject.keywordsShangri–Laen
dc.subject.keywordsClassificationen
dc.titleShangri–La: A medical case–based retrieval toolen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication33e1cf81-6a46-4cc6-828f-1c0f2a7e7497
relation.isAuthorOfPublication.latestForDiscovery33e1cf81-6a46-4cc6-828f-1c0f2a7e7497
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