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Query Expansion on Medical Image Retrieval: MeSH vs. UMLS

2009; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-642-04447-2_94

1611-3349

Manuel Carlos Díaz–Galiano, Miguel Ángel García Cumbreras, María Teresa Martín Valdivia, Luís Alfonso Ureña López, Arturo Montejo‐Ráez,

Biomedical Text Mining and Ontologies

In this paper we explain experiments in the medical information retrieval task (ImageCLEFmed). We experimented with query expansion and the amount of textual information obtained from the collection. For expansion, we carried out experiments using MeSH ontology and UMLS separately. With respect to textual collection, we produced three different collections, the first one with caption and title, the second one with caption, title and the text of the section where the image appears, and the third one with the full text article. Moreover, we experimented with textual and visual search, along with the combination of these two results. For image retrieval we used the results generated by the FIRE software. The best results were obtained using MeSH query expansion on shortest textual collection (only caption and title) merging with the FIRE results.