Learning features for tissue classification with the classification restricted Boltzmann machine
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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.
Originalsprog | Engelsk |
---|---|
Titel | 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 |
Redaktører | Bjoern Menze, Georg Langs, Albert Montillo, Michael Kelm, Henning Müller, Shaoting Zhang, Weidong (Tom) Cai, Dimitris Metaxas |
Antal sider | 12 |
Forlag | Springer |
Publikationsdato | 2014 |
Sider | 47-58 |
Kapitel | 5 |
ISBN (Trykt) | 978-3-319-13971-5 |
ISBN (Elektronisk) | 978-3-319-13972-2 |
DOI | |
Status | Udgivet - 2014 |
Begivenhed | International Workshop on Medical Computer Vision 2014 - Cambridge, USA Varighed: 18 sep. 2014 → 18 sep. 2014 |
Konference
Konference | International Workshop on Medical Computer Vision 2014 |
---|---|
Land | USA |
By | Cambridge |
Periode | 18/09/2014 → 18/09/2014 |
Navn | Lecture notes in computer science |
---|---|
ISSN | 0302-9743 |
ID: 130841164