Comparing fusion techniques for the imageCLEF 2013 medical case retrieval task

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 gure separation and modality classication 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