Medical Image Annotation in ImageCLEF

The ImageCLEF 2008 medical image annotation task is designed to assess the quality of content-based image retrieval and image classification by means of global signatures. In contrast to the previous years, the 2008 task was designed such that the hierarchy of reference IRMA code classifications is essential for good performance. In total, 12,076 images were used, and 24 runs of 6 groups were submitted. Multiclass classification schemes for support vector machines outperformed the other methods. A scoring scheme was defined to penalise wrong classification in early code positions over those in later branches of the code hierarchy, and to penalise false category association over the assignment of a “not known” code. The obtained scores rage from 74.92 over 182.77 to 313.01 for best, baseline and worst results, respectively.