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Abstract #0818

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

Ruiqi Yu1, Ying Hou2, Yang Song1, Yu-dong Zhang2, and Guang Yang1
1Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China, 2Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu, China

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%.

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