Ensemble prostate tumor classification in H&E whole slide imaging via stain normalization and cell density estimation

Abstract

Classification of prostate tumor regions in digital histology images requires comparable features across datasets. Here we introduce adaptive cell density estimation and apply H&E stain normalization into a supervised classification framework to improve inter-cohort classifier robustness. The framework uses Random Forest feature selection, class-balanced training example subsampling and support vector machine (SVM) classification to predict the presence of high- and low-grade prostate cancer (HG-PCa and LG-PCa) on image tiles. Using annotated whole-slide prostate digital pathology images to train and test on two separate patient cohorts, classification performance, as measured with area unde..