  Prostate cancer is one of the most common forms of cancer and the third
leading cause of cancer death in North America. As an integrated part of
computer-aided detection (CAD) tools, diffusion-weighted magnetic resonance
imaging (DWI) has been intensively studied for accurate detection of prostate
cancer. With deep convolutional neural networks (CNNs) significant success in
computer vision tasks such as object detection and segmentation, different CNNs
architectures are increasingly investigated in medical imaging research
community as promising solutions for designing more accurate CAD tools for
cancer detection. In this work, we developed and implemented an automated
CNNs-based pipeline for detection of clinically significant prostate cancer
(PCa) for a given axial DWI image and for each patient. DWI images of 427
patients were used as the dataset, which contained 175 patients with PCa and
252 healthy patients. To measure the performance of the proposed pipeline, a
test set of 108 (out of 427) patients were set aside and not used in the
training phase. The proposed pipeline achieved area under the receiver
operating characteristic curve (AUC) of 0.87 (95% Confidence Interval (CI):
0.84-0.90) and 0.84 (95% CI: 0.76-0.91) at slice level and patient level,
respectively.
