Incorporating UDM into Deep Learning for better PI-RADS v2 Assessment from Multi-parametric MRI

Prostate cancer is one of the most important causes of cancer-incurred deaths among males. The prostate imaging reporting and data system (PI-RADS) v2 standardizes the acquisition of multi-parametric magnetic resonance images (mp-MRI) and identification of clinically significant prostate cancer. We purposed a convolutional neural network which integrated an unsure data model (UDM) to predict the PI-RADS v2 score from mp-MRI. The model achieved an F1 score of 0.640, which is higher than that of the ResNet-50. On an independent test cohort of 146 cases, our model achieved an accuracy of 64.4%.