Learning Features for Tissue Classification with the Classification Restricted Boltzmann Machine

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8848)

Abstract

Performance of automated tissue classification in medical imaging depends on the choice of descriptive features. In this paper, we show how restricted Boltzmann machines (RBMs) can be used to learn features that are especially suited for texture-based tissue classification. We introduce the convolutional classification RBM, a combination of the existing convolutional RBM and classification RBM, and use it for discriminative feature learning. We evaluate the classification accuracy of convolutional and non-convolutional classification RBMs on two lung CT problems. We find that RBM-learned features outperform conventional RBM-based feature learning, which is unsupervised and uses only a generative learning objective, as well as often-used filter banks. We show that a mixture of generative and discriminative learning can produce filters that give a higher classification accuracy.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Biomedical Imaging Group RotterdamErasmus MC University Medical CenterRotterdamThe Netherlands
  2. 2.Image Group, Department of Computer ScienceUniversity of CopenhagenCopenhagenDenmark

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