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This article compares the performance of different convolutional neural network architectures in the patch recognition task. The study evaluates three different classification scenarios and discusses the practical considerations for predicting specific Gleason scores or a simple binary diagnosis. The results also provide insights into the system’s behavior and inter-class errors.
