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Proceedings Paper

Staining independent Bayes classifier for automated cell pattern recognition
Author(s): Xinhua Zhuang; James Lee; Yan Huang; Alan C. Nelson
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Paper Abstract

Designing the optimal Bayes classifier for automated cell pattern recognition faces two major difficulties: (1) modeling and learning the conditional probabilities P(cell features--cell type) (2) developing staining independent strategies to handle staining dependent cell features while learning those conditional probabilities. In this paper, we will show such modeling and learning techniques as well as staining independent strategies. The result of the strategies tested on an automated system designed for cervical smear screening will also be reported.

Paper Details

Date Published: 29 July 1993
PDF: 10 pages
Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); doi: 10.1117/12.148671
Show Author Affiliations
Xinhua Zhuang, Univ. of Missouri/Columbia (United States)
James Lee, NeoPath Inc. (United States)
Yan Huang, Univ. of Missouri/Columbia (United States)
Alan C. Nelson, NeoPath Inc. (United States)

Published in SPIE Proceedings Vol. 1905:
Biomedical Image Processing and Biomedical Visualization
Raj S. Acharya; Dmitry B. Goldgof, Editor(s)

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