  Prostate cancer is one of the most common causes of cancer deaths in men.
There is a growing demand for noninvasively and accurately diagnostic methods
that facilitate the current standard prostate cancer risk assessment in
clinical practice. Still, developing computer-aided classification tools in
prostate cancer diagnostics from multiparametric magnetic resonance images
continues to be a challenge. In this work, we propose a novel deep learning
approach for automatic classification of prostate lesions in the corresponding
magnetic resonance images by constructing a two-stage multimodal multi-stream
convolutional neural network (CNN)-based architecture framework. Without
implementing sophisticated image preprocessing steps or third-party software,
our framework achieved the classification performance with the area under a
Receiver Operating Characteristic (ROC) curve value of 0.87. The result
outperformed most of the submitted methods and shared the highest value
reported by the PROSTATEx Challenge organizer. Our proposed CNN-based framework
reflects the potential of assisting medical image interpretation in prostate
cancer and reducing unnecessary biopsies.
