Manual Query Modification and Data Fusion for Medical Image Retrieval

Abstract

AbstractImage retrieval has great potential for a variety of tasks in medicine but is currently underdeveloped. For the ImageCLEF 2005 medical task, we used a text retrieval system as the foundation of our experiments to assess retrieval of images from the test collection. We conducted experiments using automatic queries, manual queries, and manual queries augmented with results from visual queries. The best performance was obtained from manual modification of queries. The combination of manual and visual retrieval results resulted in lower performance based on mean average precision but higher precision within the top 30 results. Further research is needed not only to sort out the relative benefit of textual and visual methods in image retrieval but also to determine which performance measures are most relevant to the operational setting.KeywordsImage RetrievalData FusionAverage PrecisionQuery TermMean Average PrecisionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.