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.
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Marée, R., Geurts, P., Piater, J., Wehenkel, L. (2005). Biomedical Image Classification with Random Subwindows and Decision Trees. In: Liu, Y., Jiang, T., Zhang, C. (eds) Computer Vision for Biomedical Image Applications. CVBIA 2005. Lecture Notes in Computer Science, vol 3765. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569541_23
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DOI: https://doi.org/10.1007/11569541_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29411-5
Online ISBN: 978-3-540-32125-5
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