  Prostate Cancer (PCa) is a prevalent disease among men, and multi-parametric
MRIs offer a non-invasive method for its detection. While MRI-based deep
learning solutions have shown promise in supporting PCa diagnosis, acquiring
sufficient training data, particularly in local clinics remains challenging.
One potential solution is to take advantage of publicly available datasets to
pre-train deep models and fine-tune them on the local data, but multi-source
MRIs can pose challenges due to cross-domain distribution differences. These
limitations hinder the adoption of explainable and reliable deep-learning
solutions in local clinics for PCa diagnosis. In this work, we present a novel
approach for unpaired image-to-image translation of prostate multi-parametric
MRIs and an uncertainty-aware training approach for classifying clinically
significant PCa, to be applied in data-constrained settings such as local and
small clinics. Our approach involves a novel pipeline for translating unpaired
3.0T multi-parametric prostate MRIs to 1.5T, thereby augmenting the available
training data. Additionally, we introduce an evidential deep learning approach
to estimate model uncertainty and employ dataset filtering techniques during
training. Furthermore, we propose a simple, yet efficient Evidential Focal
Loss, combining focal loss with evidential uncertainty, to train our model
effectively. Our experiments demonstrate that the proposed method significantly
improves the Area Under ROC Curve (AUC) by over 20% compared to the previous
work. Our code is available at https://github.com/med-i-lab/DT_UE_PCa
