Biomedical Image Classification with Random Subwindows and Decision Trees

  • Raphaël Marée
  • Pierre Geurts
  • Justus Piater
  • Louis Wehenkel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3765)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Raphaël Marée
    • 1
  • Pierre Geurts
    • 1
  • Justus Piater
    • 1
  • Louis Wehenkel
    • 1
  1. 1.GIGA Bioinformatics Platform / CBIG, Department of EE & CS, Institut MontefioreUniversity of LiègeLiègeBelgium

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