@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",
}