Learning Features for Tissue Classification with the Classification Restricted Boltzmann Machine

  • Gijs van TulderEmail author
  • Marleen de Bruijne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8848)


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.


Classification Accuracy Hide Node Filter Bank Convolutional Neural Network Feature Learning 
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.



This research is financed by the Netherlands Organization for Scientific Research (NWO).


  1. 1.
    Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. Technical report, Université de Montréal (2012)Google Scholar
  2. 2.
    Li, Q., Cai, W., Feng, D.D.: Lung image patch classification with automatic feature learning. In: 35th Annual International Conference on Engineering in Medicine and Biology Society (EMBC) (2013)Google Scholar
  3. 3.
    Brosch, T., Tam, R.: Manifold learning of brain MRIs by deep learning. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 633–640. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Larochelle, H., Mandel, M., Pascanu, R., Bengio, Y.: Learning algorithms for the classification restricted Boltzmann machine. J. Machin. Learn. Res. 13, 643–669 (2012)zbMATHMathSciNetGoogle Scholar
  5. 5.
    Desjardins, G., Bengio, Y.: Empirical evaluation of convolutional RBMs for vision. Technical report, Université de Montréal (2008)Google Scholar
  6. 6.
    Norouzi, M., Ranjbar, M., Mori, G.: Stacks of convolutional restricted Boltzmann machines for shift-invariant feature learning. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (2009)Google Scholar
  7. 7.
    Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: The 26th International Conference on Machine Learning (ICML) (2009)Google Scholar
  8. 8.
    Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Unsupervised learning of hierarchical representations with convolutional deep belief networks. Commun. ACM 54(10), 95–103 (2011)CrossRefGoogle Scholar
  9. 9.
    Hinton, G.E.: A practical guide to training restricted Boltzmann machines. Technical report. University of Toronto (2010)Google Scholar
  10. 10.
    Pedersen, J.H., Ashraf, H., Dirksen, A., et al.: The Danish randomized lung cancer CT screening trial—overall design and results of the prevalence round. J. Thorac. Oncol. 4(5), 608–614 (2009)CrossRefGoogle Scholar
  11. 11.
    Petersen, J., Nielsen, M., Lo, P., Saghir, Z., Dirksen, A., de Bruijne, M.: Optimal graph based segmentation using flow lines with application to airway wall segmentation. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 49–60. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Depeursinge, A., Vargas, A., Platon, A., Geissbuhler, A., Poletti, P.A., Müller, H., et al.: Building a reference multimedia database for interstitial lung diseases. Comput. Med. Imaging Graph. 36(3), 227–238 (2012)CrossRefGoogle Scholar
  13. 13.
    Depeursinge, A., Van de Ville, D., Platon, A., Geissbuhler, A., Poletti, P.A., Müller, H.: Near-affine-invariant texture learning for lung tissue analysis using isotropic wavelet frames. Trans. Inf. Technol. Biomed. 16, 665–675 (2012)CrossRefGoogle Scholar
  14. 14.
    Varma, M.: A statistical approach to material classification using image patch exemplars. Trans. Pattern Anal. Mach. Intell. 31(11), 2032–2047 (2009)CrossRefGoogle Scholar
  15. 15.
    Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. Int. J. Comput. Vis. 43(1), 29–44 (2001)CrossRefzbMATHGoogle Scholar
  16. 16.
    Schmid, C.: Constructing models for content-based image retrieval. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (2001)Google Scholar
  17. 17.
    Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10(7), 1895–1923 (1998)CrossRefGoogle Scholar
  18. 18.
    Yang, Z., Sun, X., Hardin, J.W.: A note on the tests for clustered matched-pair binary data. Biometrical J. 52(5), 638–652 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  19. 19.
    Durkalski, V.L., Palesch, Y.Y., Lipsitz, S.R., Rust, P.F.: Analysis of clustered matched-pair data. Stat. Med. 22(15), 2417–2428 (2003)CrossRefGoogle Scholar
  20. 20.
    Saxe, A.M., Koh, P.W., Chen, Z., Bhand, M., Suresh, B., Ng, A.Y.: On random weights and unsupervised feature learning. In: The International Conference on Machine Learning (ICML) (2011)Google Scholar

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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