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)


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.


Test Image Training Image Biomedical Image Correct Class Tree Boost 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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