Overview of the CLEF 2009 medical image annotation track

Authors:
Tatiana Tommasi;Barbara Caputo;Petra Welter;Mark Oliver Güld;Thomas M. Deserno
Affiliations:
Idiap Research Institute, Martigny, Switzerland;Idiap Research Institute, Martigny, Switzerland;Dept. of Medical Informatics, Aachen, Germany;Dept. of Medical Informatics, Aachen, Germany;Dept. of Medical Informatics, Aachen, Germany
Venue:
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Year:
2009

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Abstract

This paper describes the last round of the medical image annotation task in ImageCLEF 2009. After four years, we defined the task as a survey of all the past experience. Seven groups participated to the challenge submitting nineteen runs. They were asked to train their algorithms on 12677 images, labelled according to four different settings, and to classify 1733 images in the four annotation frameworks. The aim is to understand how each strategy answers to the increasing number of classes and to the unbalancing. A plain classification scheme using support vector machines and local descriptors outperformed the other methods.