Baseline results for the imageCLEF 2008 medical automatic annotation task in comparison over the years

Abstract

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. A nearest-neighbor (NN) classifier is used, which uses a weighted combination of three distance and similarity measures operating on global image features: Scaled-down representations of the images are compared using models for 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. 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. © 2009 Springer Berlin Heidelberg.