ML Anthology

Unsupervised Learning in Radiology Using Novel Latent Variable Models

Luke Carrivick, Sanjay Prabhu, Paul Goddard, Jonathan Rossiter
CVPR 2005 pp. 854-859
doi:10.1109/CVPR.2005.357 /cvpr/2005/carrivick2005cvpr-unsupervised/

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.

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Cite

Text

Carrivick et al. "Unsupervised Learning in Radiology Using Novel Latent Variable Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.357

Markdown

[Carrivick et al. "Unsupervised Learning in Radiology Using Novel Latent Variable Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/carrivick2005cvpr-unsupervised/) doi:10.1109/CVPR.2005.357

BibTeX

@inproceedings{carrivick2005cvpr-unsupervised,
  title     = {{Unsupervised Learning in Radiology Using Novel Latent Variable Models}},
  author    = {Carrivick, Luke and Prabhu, Sanjay and Goddard, Paul and Rossiter, Jonathan},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2005},
  pages     = {854-859},
  doi       = {10.1109/CVPR.2005.357},
  url       = {https://mlanthology.org/cvpr/2005/carrivick2005cvpr-unsupervised/}
}