Ensemble based system for whole-slide prostate cancer probability mapping using color texture features.
AffiliationSchool of Computer Science and Informatics, University College Dublin, Ireland.
Image Interpretation, Computer-Assisted
Pattern Recognition, Automated
MetadataShow full item record
CitationEnsemble based system for whole-slide prostate cancer probability mapping using color texture features., 35 (7-8):629-45 Comput Med Imaging Graph
JournalComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
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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