Baseline Results for the CLEF 2007 Medical Automatic Annotation Task
Mark Oliver Güld, Thomas Martin Deserno · CLEF (Working Notes) · 2007
This work reports baseline results for the CLEF 2008 Medical Automatic Annotation Task (MAAT) by applying a classifier with a fixed parameter set to all tasks 2005 – 2008. The classifier performs a weighted combination of three distance and similarity measures operating on global image features: Scaled-down representations of the images are compared via metrics that model the typical variability in the image data, mainly translation, local deformation, and radiation dose. In addition, a distance measure based on texture features is used. For classification, a k nearest neighbor classifier is used. In 2008, the baseline classifier yields error scores of 170.34 and 182.77 for k=1 and k=5 when the full code is reported, which corresponds to error rates of 51.3% and 52.8% for 1-NN and 5-NN, respectively. Judging the relative increases of the number of classes and the error rates over the years, MAAT 2008 is estimated to be the most difficult in the four years.