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Scoring of breast tissue microarray spots through ordinal regression

Scoring of breast tissue microarray spots through ordinal regression

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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
Publication date2009
Pages243-248
Number of pages6
Volume2
ISBN (Print)9789898111692
StatePublished

Conference

Conference4th International Conference on Computer Vision Theory and Applications
Abbreviated titleVISAPP 2009
CountryPortugal
CityLisbon
Period5/02/098/02/09
Internet addresshttp://www.visapp.visigrapp.org/VISAPP2009/

Abstract

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.

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