Comparing fusion techniques for the ImageCLEF 2013 medical case retrieval task

Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; García Seco de Herrera, Alba (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Schaer, Roger (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Markonis, Dimitrios (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

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.


Keywords:
Article Type:
scientifique
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Date:
2015
Pagination:
pp. 46-54
Published in:
Computerized Medical Imaging and Graphics : special issue
Numeration (vol. no.):
2015, vol. 39, special issue, pp. 46-54
DOI:
ISSN:
0895-6111
Appears in Collection:

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 Record created 2015-11-23, last modified 2018-12-20

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