Computer‐aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI

• Published in: Journal of magnetic resonance imaging Vol. 48; no. 6; pp. 1570 - 1577
• Main Authors: Song, Yang, Zhang, Yu‐Dong, Yan, Xu, Liu, Hui, Zhou, Minxiong, Hu, Bingwen, Yang, Guang
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.12.2018
• Subjects: Artificial neural networks; Cancer; Confidence intervals; Decision analysis; deep convolutional neural network; Diagnosis; Diagnostic systems; Diffusion coefficient; enhanced prediction; Machine learning; Magnetic resonance imaging; Mathematical models; Medical imaging; mp‐MRI; multiparametric magnetic resonance imaging; Neural networks; Patients; Performance prediction; Prostate cancer; Rank tests; Sensitivity; Statistical analysis; Statistical tests; Statistics; prostate cancer; mp-MRI; deep convolutional neural network; enhanced prediction; multiparametric magnetic resonance imaging

Background Deep learning is the most promising methodology for automatic computer‐aided diagnosis of prostate cancer (PCa) with multiparametric MRI (mp‐MRI). Purpose To develop an automatic approach based on deep convolutional neural network (DCNN) to classify PCa and noncancerous tissues (NC) with...