  We hypothesize that anatomical priors can be viable mediums to infuse
domain-specific clinical knowledge into state-of-the-art convolutional neural
networks (CNN) based on the U-Net architecture. We introduce a probabilistic
population prior which captures the spatial prevalence and zonal distinction of
clinically significant prostate cancer (csPCa), in order to improve its
computer-aided detection (CAD) in bi-parametric MR imaging (bpMRI). To evaluate
performance, we train 3D adaptations of the U-Net, U-SEResNet, UNet++ and
Attention U-Net using 800 institutional training-validation scans, paired with
radiologically-estimated annotations and our computed prior. For 200
independent testing bpMRI scans with histologically-confirmed delineations of
csPCa, our proposed method of encoding clinical priori demonstrates a strong
ability to improve patient-based diagnosis (upto 8.70% increase in AUROC) and
lesion-level detection (average increase of 1.08 pAUC between 0.1-10 false
positives per patient) across all four architectures.
