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Pattern Recognit Lett. 2014 Jun 1;42:115-121.

Cancer diagnosis by nuclear morphometry using spatial information .

Author information

1
Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
2
Department of Pathology, Children's Hospital of Pittsburgh, Pittsburgh, PA 15224, USA.
3
Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA ; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA ; Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Abstract

Methods for extracting quantitative information regarding nuclear morphology from histopathology images have been long used to aid pathologists in determining the degree of differentiation in numerous malignancies. Most methods currently in use, however, employ the naïve Bayes approach to classify a set of nuclear measurements extracted from one patient. Hence, the statistical dependency between the samples (nuclear measurements) is often not directly taken into account. Here we describe a method that makes use of statistical dependency between samples in thyroid tissue to improve patient classification accuracies with respect to standard naïve Bayes approaches. We report results in two sample diagnostic challenges.

KEYWORDS:

Cancer diagnosis; Majority voting; Naïve Bayes; Set classification; Thyroid lesion classification

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