Gleason Score Prediction using Deep Learning in Tissue Microarray Image
This work addresses the time-consuming task for pathologists in grading prostate cancer, but it is incremental as it builds on existing methods and datasets.
The paper tackled the problem of automating Gleason score prediction for prostate cancer diagnosis by developing a CNN model that segments tissue microarray images into Gleason grades, achieving a mean Dice of 75.6% and ranking 4th in the Gleason2019 Challenge with a score of 0.778.
Prostate cancer (PCa) is one of the most common cancers in men around the world. The most accurate method to evaluate lesion levels of PCa is microscopic inspection of stained biopsy tissue and estimate the Gleason score of tissue microarray (TMA) image by expert pathologists. However, it is time-consuming for pathologists to identify the cellular and glandular patterns for Gleason grading in large TMA images. We used Gleason2019 Challenge dataset to build a convolutional neural network (CNN) model to segment TMA images to regions of different Gleason grades and predict the Gleason score according to the grading segmentation. We used a pre-trained model of prostate segmentation to increase the accuracy of the Gleason grade segmentation. The model achieved a mean Dice of 75.6% on the test cohort and ranked 4th in the Gleason2019 Challenge with a score of 0.778 combined of Cohen's kappa and the f1-score.