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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. Technical report, Université de Montréal (2012)
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)
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)
Larochelle, H., Mandel, M., Pascanu, R., Bengio, Y.: Learning algorithms for the classification restricted Boltzmann machine. J. Machin. Learn. Res. 13, 643–669 (2012)
Desjardins, G., Bengio, Y.: Empirical evaluation of convolutional RBMs for vision. Technical report, Université de Montréal (2008)
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)
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)
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)
Hinton, G.E.: A practical guide to training restricted Boltzmann machines. Technical report. University of Toronto (2010)
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)
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)
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)
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)
Varma, M.: A statistical approach to material classification using image patch exemplars. Trans. Pattern Anal. Mach. Intell. 31(11), 2032–2047 (2009)
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)
Schmid, C.: Constructing models for content-based image retrieval. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (2001)
Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10(7), 1895–1923 (1998)
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)
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)
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)
Acknowledgment
This research is financed by the Netherlands Organization for Scientific Research (NWO).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
van Tulder, G., de Bruijne, M. (2014). Learning Features for Tissue Classification with the Classification Restricted Boltzmann Machine. In: Menze, B., et al. Medical Computer Vision: Algorithms for Big Data. MCV 2014. Lecture Notes in Computer Science(), vol 8848. Springer, Cham. https://doi.org/10.1007/978-3-319-13972-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-13972-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13971-5
Online ISBN: 978-3-319-13972-2
eBook Packages: Computer ScienceComputer Science (R0)