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dc.contributor.authorDiFranco, Matthew D
dc.contributor.authorO'Hurley, Gillian
dc.contributor.authorKay, Elaine W
dc.contributor.authorWatson, R William G
dc.contributor.authorCunningham, Padraig
dc.date.accessioned2012-06-15T15:34:04Z
dc.date.available2012-06-15T15:34:04Z
dc.date.issued2011
dc.identifier.citationEnsemble based system for whole-slide prostate cancer probability mapping using color texture features., 35 (7-8):629-45 Comput Med Imaging Graphen_GB
dc.identifier.issn1879-0771
dc.identifier.pmid21269807
dc.identifier.doi10.1016/j.compmedimag.2010.12.005
dc.identifier.urihttp://hdl.handle.net/10147/229196
dc.description.abstractWe present a tile-based approach for producing clinically relevant probability maps of prostatic carcinoma in histological sections from radical prostatectomy. Our methodology incorporates ensemble learning for feature selection and classification on expert-annotated images. Random forest feature selection performed over varying training sets provides a subset of generalized CIEL*a*b* co-occurrence texture features, while sample selection strategies with minimal constraints reduce training data requirements to achieve reliable results. Ensembles of classifiers are built using expert-annotated tiles from training images, and scores for the probability of cancer presence are calculated from the responses of each classifier in the ensemble. Spatial filtering of tile-based texture features prior to classification results in increased heat-map coherence as well as AUC values of 95% using ensembles of either random forests or support vector machines. Our approach is designed for adaptation to different imaging modalities, image features, and histological decision domains.
dc.language.isoenen
dc.rightsArchived with thanks to Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Societyen_GB
dc.subject.meshAlgorithms
dc.subject.meshColor
dc.subject.meshHistological Techniques
dc.subject.meshHumans
dc.subject.meshImage Interpretation, Computer-Assisted
dc.subject.meshMale
dc.subject.meshPattern Recognition, Automated
dc.subject.meshProstatic Neoplasms
dc.titleEnsemble based system for whole-slide prostate cancer probability mapping using color texture features.en_GB
dc.typeArticleen
dc.contributor.departmentSchool of Computer Science and Informatics, University College Dublin, Ireland.en_GB
dc.identifier.journalComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Societyen_GB
dc.description.provinceLeinsteren
html.description.abstractWe present a tile-based approach for producing clinically relevant probability maps of prostatic carcinoma in histological sections from radical prostatectomy. Our methodology incorporates ensemble learning for feature selection and classification on expert-annotated images. Random forest feature selection performed over varying training sets provides a subset of generalized CIEL*a*b* co-occurrence texture features, while sample selection strategies with minimal constraints reduce training data requirements to achieve reliable results. Ensembles of classifiers are built using expert-annotated tiles from training images, and scores for the probability of cancer presence are calculated from the responses of each classifier in the ensemble. Spatial filtering of tile-based texture features prior to classification results in increased heat-map coherence as well as AUC values of 95% using ensembles of either random forests or support vector machines. Our approach is designed for adaptation to different imaging modalities, image features, and histological decision domains.


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