Ensemble Prostate Tumor Classification in H&E Whole Slide Imaging via Stain Normalization and Cell Density Estimation

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 under the ROC curve AUC, was 0.703 for HG-PCa and 0.705 for LG-PCa. These results improve upon previous work and demonstrate the effectiveness of cell-density and stain normalization on classification of prostate digital slides across cohorts.