The development of CAD systems for prostate cancer detection requires large amounts of training data with correlated pathologic ground truth. The gold standard is manual annotation of cancer by pathologists, which is tedious and difficult to obtain. Here, we retrospectively applied a previously-described digital-pathology framework for automating cancer annotation. We trained a Bayesian predictive model on the original ground truth (from manual annotation) and on the new ground truth, and compared the performances. The results suggest the ground truths are very similar and largely equivalent, which provides support for prospective usage of our approach for automatic annotation of prostate cancer.
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