ARTICLE Tommasi_PRL_2008/IDIAP Discriminative cue integration for medical image annotation Tommasi, Tatiana Orabona, Francesco Caputo, Barbara EXTERNAL PUBLIC Pattern Recognition Letters 2008 Special Issue on Automatic Annotation of Medical Images (ImageCLEF 2007, in Press Automatic annotation of medical images is an increasingly important tool for physicians in their daily activity. Hospitals nowadays produce an increasing amount of data. Manual annotation is very costly and prone to human mistakes. This paper proposes a multi-cue approach to automatic medical image annotation. We represent images using global and local features. These cues are then combined using three alternative approaches, all based on the Support Vector Machine algorithm. We tested our methods on the IRMA database, and with two of the three approaches proposed here we participated in the 2007 ImageCLEFmed benchmark evaluation, in the medical image annotation track. These algorithms ranked first and fifth respectively among all submission. Experiments using the third approach also confirm the power of cue integration for this task.