Publicación: Comparing fusion techniques for the ImageCLEF 2013 medical case retrieval task
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2014-03-27
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info:eu-repo/semantics/openAccess
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Elsevier
Resumen
Retrieval systems can supply similar cases with a proven diagnosis to a new example case under observation to help clinicians during their work. The ImageCLEFmed evaluation campaign proposes a framework where research groups can compare case–based retrieval approaches. This paper focuses on the case–based task and adds results of the compound figure separation and modality classification tasks. Several fusion approaches are compared to identify the approaches best adapted to the heterogeneous data of the task. Fusion of visual and textual features is analyzed, demonstrating that the selection of the fusion strategy can improve the best performance on the case–based retrieval task.
Descripción
Esta es la versión aceptada del artículo. La versión registrada fue publicada por primera vez en Computerized Medical Imaging and Graphics 39 (2015): 46-54, está disponible en línea en el sitio web del editor: Elsevier: https://doi.org/10.1016/J.COMPMEDIMAG.2014.04.004.
This is the accepted version of the article. The registered version was first published in Computerized Medical Imaging and Graphics 39 (2015): 46-54, is available online at the publisher's website: Elsevier: https://doi.org/10.1016/J.COMPMEDIMAG.2014.04.004.
Categorías UNESCO
Palabras clave
Medical Case–based retrieval, Multimodal Fusion, Visual Reranking, ImageCLEF, medGIFT
Citación
Alba García Seco de Herrera, Roger Schaer, Dimitrios Markonis, y Henning Müller. «Comparing fusion techniques for the ImageCLEF 2013 medical case retrieval task». Computerized Medical Imaging and Graphics 39 (2015): 46-54. https://doi.org/10.1016/J.COMPMEDIMAG.2014.04.004
Centro
E.T.S. de Ingeniería Informática
Departamento
Lenguajes y Sistemas Informáticos