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 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.
H.M. Reynolds—Funded by a Movember Young Investigator Grant awarded through Prostate Cancer Foundation of Australia Research Program.
A. Haworth—Supported by PdCCRS grant 628592 with funding partners: Prostate Cancer Foundation of Australia; Radiation Oncology Section of the Australian Government of Health and Ageing; Cancer Australia.
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Weingant, M., Reynolds, H.M., Haworth, A., Mitchell, C., Williams, S., DiFranco, M.D. (2015). Ensemble Prostate Tumor Classification in H&E Whole Slide Imaging via Stain Normalization and Cell Density Estimation. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds) Machine Learning in Medical Imaging. MLMI 2015. Lecture Notes in Computer Science(), vol 9352. Springer, Cham. https://doi.org/10.1007/978-3-319-24888-2_34
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DOI: https://doi.org/10.1007/978-3-319-24888-2_34
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