Comparing fusion techniques for the ImageCLEF 2013 medical case retrieval task. (January 2015)

• Record Type: Journal Article
• Authors: G. Seco de Herrera, Alba; Schaer, Roger; Markonis, Dimitrios; Müller, Henning
• Is Part Of: Computerized medical imaging and graphics. Volume 39(2015)
• Page Start: 46
• Page End: 54
• Publication Date: 2015-01
• Subjects: Medical case-based retrieval -- Multimodal fusion -- Visual reranking -- ImageCLEF -- MedGIFT; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2014.04.004
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: 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.