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Scoring of breast tissue microarrays using ordinal regression

Scoring of breast tissue microarrays using ordinal regression: local patches vs. nuclei segmentation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Authors

  • Telmo Amaral
  • Michele Sciarabba
  • Katherine Robertson
  • Alastair Thompson
  • Stephen McKenna

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Info

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis
PublisherBritish Machine Vision Association and Society for Pattern Recognition
Number of pages6
StatePublished - 2009

Abstract

Breast tissue microarrays (TMAs) facilitate the study of very large numbers of breast tumours per histological section, but their scoring by pathologists is time consuming, prone to observer variability, and not without error. This paper reports the use of ordinal regression to predict the scores of TMA spots subjected to progesterone receptor immunohistochemistry. We compare the use of global features obtained via two different methods, one involving and the other dispensing with accurate segmentation of epithelial cell nuclei. In addition, we investigate the effect of analysing only regions of interest (ROIs) within each spot, as opposed to analysing the whole spots. The use of the entropy of the posterior probability distribution over category labels for avoiding uncertain decisions is demonstrated.

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