Prostate Histopathology: Learning Tissue Component Histograms for Cancer Detection and Classification

Abstract—Radical prostatectomy is performed on approxi-mately 40 % of men with organ-confined prostate cancer. Patho-logic information obtained from the prostatectomy specimen provides important prognostic information and guides recommen-dations for adjuvant treatment. The current pathology protocol in most centers involves primarily qualitative assessment. In this paper, we describe and evaluate our system for automatic prostate cancer detection and grading on hematoxylin & eosin-stained tissue images. Our approach is intended to address the dual challenges of large data size and the need for high-level tissue information about the locations and grades of tumors. Our system uses two stages of AdaBoost-based classification. The first provides high-level tissue component labeling of a superpixel image parti-tioning. The second uses the tissue component labeling to provide a classification of cancer versus noncancer, and low-grade versu