Scoring of breast tissue microarray spots through ordinal regression

Telmo Amaral, Stephen J. McKenna, Katherine Robertson, Alastair Thompson

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

    2 Citations (Scopus)


    Breast tissue microarrays (TMAs) facilitate the study of very large numbers of breast tumours in a single histological section, but their scoring by pathologists is time consuming, typically highly quantised, and not without error. This paper compares the results of different classification and ordinal regression algorithms trained to predict the scores of immunostained breast TMA spots, based on spot features obtained in previous work by the authors. Despite certain theoretical advantages. Gaussian process ordinal regression failed to achieve any clear performance gain over classification using a multi-layer perceptron. The use of the entropy of the posterior probability distribution over class labels for avoiding uncertain decisions is demonstrated.

    Original languageEnglish
    Title of host publicationVisapp 2009: Proceedings of The Fourth International Conference on Computer Vision Theory and Applications, Vol 2
    Place of PublicationLisbon
    PublisherInstitute for Systems and Technologies of Information, Control and Communication
    Number of pages6
    ISBN (Print)9789898111692
    Publication statusPublished - 2009
    Event4th International Conference on Computer Vision Theory and Applications - Lisboa, Lisbon, Portugal
    Duration: 5 Feb 20098 Feb 2009


    Conference4th International Conference on Computer Vision Theory and Applications
    Abbreviated titleVISAPP 2009
    Internet address


    • Breast tissue microarrays
    • Scoring
    • Immunohistochemistry
    • Ordinal regression
    • Validation
    • Carcinoma
    • Grade

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