Overview of the ImageCLEFmed 2007 Medical Retrieval and Medical Annotation Tasks

This paper describes the medial image retrieval and the medical annotation tasks of ImageCLEF 2006. These tasks are described in a separate paper from the other task to reduce the size of the overview papaer.These two medical tasks are described separately with respect to the goals, databases used, topics created and distributed among participants, results and techniques used. The best performing techniques are described in more detail to provide better insights about successful strategies. Some ideas for future tasks are also presented. The ImageCLEFmed medical image retrieval task had 12 participating groups and received 100 submitted runs. Most runs were automatic, with only a few manual or interactive. Purely textual runs were in the majority compared to purely visual, runs but most runs were mixed, i.e., using visual and textual information. None of the manual or interactive techniques were significantly better than those used for the automatic runs. The best-performing systems used visual and textual techniques combined, but combinations of visual and textual features often did not improve a system's performance. Purely visual systems only performed well on the visual topics. The medical automatic annotation used a larger database in 2006, with 10'000 training images and 116 classes, up from 57 in 2005. Twelve participating groups submitted 27 runs. Despite the much larger number of classes, results were almost as good as in 2005 and a clear improvement in performance could be shown. The best- performing system of 2005 would have only received a position in the upper middle part in 2006.