title: Cancer diagnosis by nuclear morphometry using spatial information.
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 thenaïve Bayesapproach 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 standardnaïve Bayesapproaches. We report results in two sample diagnostic challenges.
authors.affiliation: Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
authors.forename: Hu
authors.initials: H
authors.lastname: Huang
authors.affiliation: Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
authors.forename: Akif Burak
authors.initials: AB
authors.lastname: Tosun
authors.affiliation: Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
authors.forename: Jia
authors.initials: J
authors.lastname: Guo
authors.affiliation: Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
authors.forename: Cheng
authors.initials: C
authors.lastname: Chen
authors.affiliation: Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.affiliation: Department of Pathology, Children's Hospital of Pittsburgh, Pittsburgh, PA 15224, USA.
authors.forename: John A
authors.initials: JA
authors.lastname: Ozolek
authors.affiliation: 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.
authors.forename: Gustavo K
authors.initials: GK
authors.lastname: Rohde
keywords: Cancer diagnosis; Majority voting; Naïve Bayes; Set classification; Thyroid lesion classification