کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
504350 | 864295 | 2011 | 17 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Ensemble based system for whole-slide prostate cancer probability mapping using color texture features Ensemble based system for whole-slide prostate cancer probability mapping using color texture features](/preview/png/504350.png)
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.
Journal: Computerized Medical Imaging and Graphics - Volume 35, Issues 7–8, October–December 2011, Pages 629–645