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Prostate cancer (PCa) is one of the most common cancers in men around theworld. The most accurate method to evaluate lesion levels of PCa is microscopicinspection of stained biopsy tissue and estimate the Gleason score of tissuemicroarray (TMA) image by expert pathologists. However, it is time-consumingfor pathologists to identify the cellular and glandular patterns for Gleasongrading in large TMA images. We used Gleason2019 Challenge dataset to build aconvolutional neural network (CNN) model to segment TMA images to regions ofdifferent Gleason grades and predict the Gleason score according to the gradingsegmentation. We used a pre-trained model of prostate segmentation to increasethe accuracy of the Gleason grade segmentation. The model achieved a mean Diceof 75.6% on the test cohort and ranked 4th in the Gleason2019 Challenge with ascore of 0.778 combined of Cohen's kappa and the f1-score.

Gleason Score Prediction using Deep Learning in Tissue Microarray Image