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.

本文介绍了基于卷积神经网络（CNNs）构建自动化流程，用于检测临床意义的前列腺癌（PCa）的给定轴向 DWI 图像，并为每个患者进行检测。检测性能在受试患者中获得了 0.87 的接收器操作特征曲线下面积（AUC）（95％置信区间（CI）：0.84-0.90），在切片级别和患者级别分别为 0.84（95％CI：0.76-0.91）。