Prostate Cancer Diagnosis and Gleason Grading of Histological Images

By Wenyuan Li
Ph.D. of Electrical and Computer Engineering

Prostate cancer is the most common and second most deadly form of cancer in men in the United States. Pathologists use several screening methodologies to qualitatively describe the diverse tumor histology in the prostate. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. In this study, we demonstrate a new region-based convolutional neural network (R-CNN) framework for multi-task prediction using an Epithelial Network Head and a Grading Network Head. Compared to a single task model, our multi-task model can provide complementary contextual information, which contributes to better performance. Our model achieved state-of-the-art performance in epithelial cells detection and Gleason grading tasks simultaneously. Using five-fold cross-validation, our model achieved an epithelial cells detection accuracy of 99.07% with an average AUC of 0.998. As for Gleason grading, our model obtained a mean intersection over union of 79.56% and an overall pixel accuracy of 89.40%.