Biomedical image classification with random subwindows and decision trees

Authors:
Raphaël Marée;Pierre Geurts;Justus Piater;Louis Wehenkel
Affiliations:
GIGA Bioinformatics Platform / CBIG, Department of EE & CS, Institut Montefiore, University of Liège, Liège, Belgium;GIGA Bioinformatics Platform / CBIG, Department of EE & CS, Institut Montefiore, University of Liège, Liège, Belgium;GIGA Bioinformatics Platform / CBIG, Department of EE & CS, Institut Montefiore, University of Liège, Liège, Belgium;GIGA Bioinformatics Platform / CBIG, Department of EE & CS, Institut Montefiore, University of Liège, Liège, Belgium
Venue:
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Year:
2005

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Abstract

In this paper, we address a problem of biomedical image classification that involves the automatic classification of x-ray images in 57 predefined classes with large intra-class variability. To achieve that goal, we apply and slightly adapt a recent generic method for image classification based on ensemble of decision trees and random subwindows. We obtain classification results close to the state of the art on a publicly available database of 10000 x-ray images. We also provide some clues to interpret the classification of each image in terms of subwindow relevance.