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Learning features for tissue classification with the classification restricted Boltzmann machine. / van Tulder, Gijs; de Bruijne, Marleen.
Medical Computer Vision: Algorithms for Big Data: International Workshop, MCV 2014, Held in Conjunction with MICCAI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers. ed. / Bjoern Menze; Georg Langs; Albert Montillo; Michael Kelm; Henning Müller; Shaoting Zhang; Weidong (Tom) Cai; Dimitris Metaxas. Springer, 2014. p. 47-58 (Lecture notes in computer science).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
van Tulder, G
& de Bruijne, M 2014,
Learning features for tissue classification with the classification restricted Boltzmann machine. in B Menze, G Langs, A Montillo, M Kelm, H Müller, S Zhang, WT Cai & D Metaxas (eds),
Medical Computer Vision: Algorithms for Big Data: International Workshop, MCV 2014, Held in Conjunction with MICCAI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers. Springer, Lecture notes in computer science, pp. 47-58, International Workshop on Medical Computer Vision 2014, Cambridge, United States,
18/09/2014.
https://doi.org/10.1007/978-3-319-13972-2_5APA
van Tulder, G.
, & de Bruijne, M. (2014).
Learning features for tissue classification with the classification restricted Boltzmann machine. In B. Menze, G. Langs, A. Montillo, M. Kelm, H. Müller, S. Zhang, W. T. Cai, & D. Metaxas (Eds.),
Medical Computer Vision: Algorithms for Big Data: International Workshop, MCV 2014, Held in Conjunction with MICCAI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers (pp. 47-58). Springer. Lecture notes in computer science
https://doi.org/10.1007/978-3-319-13972-2_5Vancouver
van Tulder G
, de Bruijne M.
Learning features for tissue classification with the classification restricted Boltzmann machine. In Menze B, Langs G, Montillo A, Kelm M, Müller H, Zhang S, Cai WT, Metaxas D, editors, Medical Computer Vision: Algorithms for Big Data: International Workshop, MCV 2014, Held in Conjunction with MICCAI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers. Springer. 2014. p. 47-58. (Lecture notes in computer science).
https://doi.org/10.1007/978-3-319-13972-2_5Author
van Tulder, Gijs ; de Bruijne, Marleen. / Learning features for tissue classification with the classification restricted Boltzmann machine. Medical Computer Vision: Algorithms for Big Data: International Workshop, MCV 2014, Held in Conjunction with MICCAI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers. editor / Bjoern Menze ; Georg Langs ; Albert Montillo ; Michael Kelm ; Henning Müller ; Shaoting Zhang ; Weidong (Tom) Cai ; Dimitris Metaxas. Springer, 2014. pp. 47-58 (Lecture notes in computer science).
Bibtex
@inproceedings{0042145b62b145fab22b90322f17078e,
title = "Learning features for tissue classification with the classification restricted Boltzmann machine",
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.",
author = "{van Tulder}, Gijs and {de Bruijne}, Marleen",
year = "2014",
doi = "10.1007/978-3-319-13972-2_5",
language = "English",
isbn = "978-3-319-13971-5",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "47--58",
editor = "Bjoern Menze and Georg Langs and Albert Montillo and Michael Kelm and Henning M{\"u}ller and Shaoting Zhang and Cai, {Weidong (Tom)} and Dimitris Metaxas",
booktitle = "Medical Computer Vision: Algorithms for Big Data",
address = "Switzerland",
note = "International Workshop on Medical Computer Vision 2014, MCV 2014 ; Conference date: 18-09-2014 Through 18-09-2014",
}
RIS
TY - GEN
T1 - Learning features for tissue classification with the classification restricted Boltzmann machine
AU - van Tulder, Gijs
AU - de Bruijne, Marleen
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-319-13972-2_5
DO - 10.1007/978-3-319-13972-2_5
M3 - Article in proceedings
AN - SCOPUS:84917707434
SN - 978-3-319-13971-5
T3 - Lecture notes in computer science
SP - 47
EP - 58
BT - Medical Computer Vision: Algorithms for Big Data
A2 - Menze, Bjoern
A2 - Langs, Georg
A2 - Montillo, Albert
A2 - Kelm, Michael
A2 - Müller, Henning
A2 - Zhang, Shaoting
A2 - Cai, Weidong (Tom)
A2 - Metaxas, Dimitris
PB - Springer
T2 - International Workshop on Medical Computer Vision 2014
Y2 - 18 September 2014 through 18 September 2014
ER -
