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

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

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

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

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