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

Authors:
Mark O. Güld;Petra Welter;Thomas M. Deserno
Affiliations:
Department of Medical Informatics, RWTH Aachen University, Aachen, Germany;Department of Medical Informatics, RWTH Aachen University, Aachen, Germany;Department of Medical Informatics, RWTH Aachen University, Aachen, Germany
Venue:
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Year:
2008

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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.