DocumentCode :
3549183
Title :
Unsupervised learning in radiology using novel latent variable models
Author :
Carrivick, Luke ; Prabhu, Sanjay ; Goddard, Paul ; Rossiter, Jonathan
Author_Institution :
Dept. of Eng. Math., Bristol Univ., UK
Volume :
2
fYear :
2005
fDate :
20-25 June 2005
Firstpage :
854
Abstract :
In this paper we compare a variety of unsupervised probabilistic models used to represent a data set consisting of textual and image information. We show that those based on latent Dirichlet allocation (LDA) out perform traditional mixture models in likelihood comparison. The data set is taken from radiology; a combination of medical images and consultants reports. The task of learning to classify individual tissue, or disease types, requires expert hand labeled data. This is both: expensive to produce and prone to inconsistencies in labeling. Here we present methods that require no hand labeling and also automatically discover sub-types of disease. The learnt models can be used for both prediction and classification of new unseen data.
Keywords :
diseases; image classification; image texture; learning (artificial intelligence); medical expert systems; medical image processing; probability; radiology; consultants reports; disease; latent Dirichlet allocation; medical images; novel latent variable models; radiology; textual information; unsupervised learning; unsupervised probabilistic models; Biomedical imaging; Computed tomography; Data engineering; Diseases; History; Image converters; Labeling; Medical diagnostic imaging; Radiology; Unsupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on
ISSN :
1063-6919
Print_ISBN :
0-7695-2372-2
Type :
conf
DOI :
10.1109/CVPR.2005.357
Filename :
1467532
Link To Document :
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