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CLEF
2007
Springer

MIRACLE at ImageCLEFanot 2007: Machine Learning Experiments on Medical Image Annotation

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MIRACLE at ImageCLEFanot 2007: Machine Learning Experiments on Medical Image Annotation
This paper describes the participation of MIRACLE research consortium at the ImageCLEF Medical Image Annotation task of ImageCLEF 2007. Our areas of expertise do not include image analysis, thus we approach this task as a machine-learning problem, regardless of the domain. FIRE is used as a black-box algorithm to extract different groups of image features that are later used for training different classifiers in order to predict the IRMA code. Three types of classifiers are built. The first type is a single classifier that predicts the complete IRMA code. The second type is a two level classifier composed of four classifiers that individually predict each axis of the IRMA code. The third type is similar to the second one but predicts a combined pair of axes. The main idea behind the definition of our experiments is to evaluate whether an axis-by-axis prediction is better than a prediction by pairs of axes or the complete code, or vice versa. We submitted 30 experiments to be evaluated...
Sara Lana-Serrano, Julio Villena-Román, Jos
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where CLEF
Authors Sara Lana-Serrano, Julio Villena-Román, José Carlos González Cristóbal, José Miguel Goñi-Menoyo
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