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

翻译：前列腺癌（PCa）是男性常见疾病，多参数磁共振成像（MRI）为其检测提供了非侵入性方法。尽管基于MRI的深度学习解决方案在前列腺癌辅助诊断中展现出潜力，但获取充足训练数据（尤其在地方诊所）仍具挑战性。利用公开数据集预训练深度模型并在本地数据上微调是潜在解决方案，但多源MRI数据因跨域分布差异可能带来挑战。这些限制阻碍了可解释且可靠的深度学习解决方案在前列腺癌地方诊所诊断中的推广应用。本研究提出一种创新的非配对前列腺多参数MRI图像到图像转换方法，以及用于临床显著性前列腺癌分类的不确定性感知训练方案，适用于地方诊所等数据受限场景。我们的方法包含将非配对的3.0T多参数前列腺MRI转换为1.5T的创新流程，从而扩充可用训练数据。此外，我们引入证据深度学习方法来估计模型不确定性，并在训练中采用数据集过滤技术。进一步提出简洁高效的证据焦点损失函数，将焦点损失与证据不确定性相结合以优化模型训练。实验表明，与现有研究相比，该方法将受试者工作特征曲线下面积（AUC）显著提升超过20%。代码已开源：https://github.com/med-i-lab/DT_UE_PCa