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.
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Güld, M.O., Welter, P., Deserno, T.M. (2009). Baseline Results for the ImageCLEF 2008 Medical Automatic Annotation Task in Comparison over the Years. In: Peters, C., et al. Evaluating Systems for Multilingual and Multimodal Information Access. CLEF 2008. Lecture Notes in Computer Science, vol 5706. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04447-2_97
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DOI: https://doi.org/10.1007/978-3-642-04447-2_97
Publisher Name: Springer, Berlin, Heidelberg
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