ML Anthology

Predicting the Histology of Colorectal Lesions in a Probabilistic Framework

Roland Kwitt, Andreas Uhl, Michael Häfner, Alfred Gangl, Friedrich Wrba, Andreas Vécsei
CVPRW 2010 pp. 103-110
doi:10.1109/CVPRW.2010.5543146 /cvprw/2010/kwitt2010cvprw-predicting/

Abstract

In this paper, we present a novel approach to predict the histological diagnosis of colorectal lesions from high-magnification colonoscopy images by means of Pit Pattern analysis. Motivated by the shortcomings of discriminant classifier approaches, we present a generative model based strategy which is closely related to content-based image retrieval (CBIR) systems. The ingredients of the approach are the Dual-Tree Complex Wavelet Transform (DTCWT) and the mathematical construct of copulas. Our experimental study on a set of 627 images confirms, that the joint statistical model leads to impressive prediction results compared to previous work.

CVPRW Semantic Scholar

Cite

Text

Kwitt et al. "Predicting the Histology of Colorectal Lesions in a Probabilistic Framework." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010. doi:10.1109/CVPRW.2010.5543146

Markdown

[Kwitt et al. "Predicting the Histology of Colorectal Lesions in a Probabilistic Framework." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010.](https://mlanthology.org/cvprw/2010/kwitt2010cvprw-predicting/) doi:10.1109/CVPRW.2010.5543146

BibTeX

@inproceedings{kwitt2010cvprw-predicting,
  title     = {{Predicting the Histology of Colorectal Lesions in a Probabilistic Framework}},
  author    = {Kwitt, Roland and Uhl, Andreas and Häfner, Michael and Gangl, Alfred and Wrba, Friedrich and Vécsei, Andreas},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2010},
  pages     = {103-110},
  doi       = {10.1109/CVPRW.2010.5543146},
  url       = {https://mlanthology.org/cvprw/2010/kwitt2010cvprw-predicting/}
}