title: Ensemble based system for whole-slide prostate cancer probability mapping using color texture features.
abstract: We 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.
authors.affiliation: School of Computer Science and Informatics, University College Dublin, Ireland.
authors.forename: Matthew D
authors.initials: MD
authors.lastname: DiFranco
authors.forename: Gillian
authors.initials: G
authors.lastname: O'Hurley
authors.forename: Elaine W
authors.initials: EW
authors.lastname: Kay
authors.forename: R William G
authors.initials: RW
authors.lastname: Watson
authors.forename: Padraig
authors.initials: P
authors.lastname: Cunningham
mesh_terms: D000465:Algorithms; D003116:Color; D006652:Histological Techniques / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008297:Male; D010363:Pattern Recognition, Automated; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000473:pathology*