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Prostate cancer (PCa) is one of the most prevalent cancers in men and manypeople around the world die from clinically significant PCa (csPCa). Earlydiagnosis of csPCa in bi-parametric MRI (bpMRI), which is non-invasive,cost-effective, and more efficient compared to multiparametric MRI (mpMRI), cancontribute to precision care for PCa. The rapid rise in artificial intelligence(AI) algorithms are enabling unprecedented improvements in providing decisionsupport systems that can aid in csPCa diagnosis and understanding. However,existing state of the art AI algorithms which are based on deep learningtechnology are often limited to 2D images that fails to capture inter-slicecorrelations in 3D volumetric images. The use of 3D convolutional neuralnetworks (CNNs) partly overcomes this limitation, but it does not adapt to theanisotropy of images, resulting in sub-optimal semantic representation and poorgeneralization. Furthermore, due to the limitation of the amount of labelleddata of bpMRI and the difficulty of labelling, existing CNNs are built onrelatively small datasets, leading to a poor performance. To address thelimitations identified above, we propose a new Zonal-aware Self-supervised MeshNetwork (Z-SSMNet) that adaptatively fuses multiple 2D, 2.5D and 3D CNNs toeffectively balance representation for sparse inter-slice information and denseintra-slice information in bpMRI. A self-supervised learning (SSL) technique isfurther introduced to pre-train our network using unlabelled data to learn thegeneralizable image features. Furthermore, we constrained our network tounderstand the zonal specific domain knowledge to improve the diagnosisprecision of csPCa. Experiments on the PI-CAI Challenge dataset demonstrate ourproposed method achieves better performance for csPCa detection and diagnosisin bpMRI.

Z-SSMNet: A Zonal-aware Self-Supervised Mesh Network for Prostate Cancer Detection and Diagnosis in bpMRI