Shangri–La: A medical case–based retrieval tool

Large 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 informa...

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Detalles Bibliográficos
Autores: García Seco de Herrera, Alba, Schaer, Roger, Müller, Henning
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universidad Nacional de Educación a Distancia
Repositorio:e-spacio. Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/26369
Acceso en línea:https://hdl.handle.net/20.500.14468/26369
Access Level:acceso abierto
Palabra clave:12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
Medical visual information retrieval
ImageCLEF
Medical case retrieval
Query adaptive multi–modal fusion
Shangri–La
Classification
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oai_identifier_str oai:e-spacio.uned.es:20.500.14468/26369
network_acronym_str ES
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repository_id_str
spelling Shangri–La: A medical case–based retrieval toolGarcía Seco de Herrera, AlbaSchaer, RogerMüller, Henning12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 InformáticaMedical visual information retrievalImageCLEFMedical case retrievalQuery adaptive multi–modal fusionShangri–LaClassificationLarge 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.Wileye-Spacio UNED20252025-03-2720182018-11-2820182018-11-28journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14468/26369reponame:e-spacio. Repositorio Institucional de la UNEDinstname:Universidad Nacional de Educación a DistanciaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.esoai:e-spacio.uned.es:20.500.14468/263692026-06-06T12:38:31Z
dc.title.none.fl_str_mv Shangri–La: A medical case–based retrieval tool
title Shangri–La: A medical case–based retrieval tool
spellingShingle Shangri–La: A medical case–based retrieval tool
García Seco de Herrera, Alba
12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
Medical visual information retrieval
ImageCLEF
Medical case retrieval
Query adaptive multi–modal fusion
Shangri–La
Classification
title_short Shangri–La: A medical case–based retrieval tool
title_full Shangri–La: A medical case–based retrieval tool
title_fullStr Shangri–La: A medical case–based retrieval tool
title_full_unstemmed Shangri–La: A medical case–based retrieval tool
title_sort Shangri–La: A medical case–based retrieval tool
dc.creator.none.fl_str_mv García Seco de Herrera, Alba
Schaer, Roger
Müller, Henning
author García Seco de Herrera, Alba
author_facet García Seco de Herrera, Alba
Schaer, Roger
Müller, Henning
author_role author
author2 Schaer, Roger
Müller, Henning
author2_role author
author
dc.contributor.none.fl_str_mv e-Spacio UNED
dc.subject.none.fl_str_mv 12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
Medical visual information retrieval
ImageCLEF
Medical case retrieval
Query adaptive multi–modal fusion
Shangri–La
Classification
topic 12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
Medical visual information retrieval
ImageCLEF
Medical case retrieval
Query adaptive multi–modal fusion
Shangri–La
Classification
description Large 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.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-11-28
2018
2018-11-28
2025
2025-03-27
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/26369
url https://hdl.handle.net/20.500.14468/26369
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:e-spacio. Repositorio Institucional de la UNED
instname:Universidad Nacional de Educación a Distancia
instname_str Universidad Nacional de Educación a Distancia
reponame_str e-spacio. Repositorio Institucional de la UNED
collection e-spacio. Repositorio Institucional de la UNED
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812455

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