Prostate cancer is a commonly diagnosed cancerous disease among men world-wide. Even with modern technology such as multi-parametric magnetic resonance tomography and guided biopsies, the process for diagnosing prostate cancer remains time consuming and requires highly trained professionals. In this paper, different convolutional neural networks (CNN) are evaluated on their abilities to reliably classify whether an MRI sequence contains malignant lesions. Implementations of a ResNet, a ConvNet and a ConvNeXt for 3D image data are trained and evaluated. The models are trained using different data augmentation techniques, learning rates, and optimizers. The data is taken from a private dataset, provided by Cantonal Hospital Aarau. The best result was achieved by a ResNet3D, yielding an average precision score of 0.4583 and AUC ROC score of 0.6214.

翻译：前列腺癌是全球男性中常见的一种癌症性疾病。尽管采用了多参数磁共振断层成像和引导活检等现代技术，前列腺癌的诊断过程仍然耗时且需要高度专业化的医务人员。本文评估了不同卷积神经网络（CNN）在可靠分类磁共振序列是否包含恶性病变方面的能力。针对三维图像数据，我们训练并评估了ResNet、ConvNet和ConvNeXt的实现。这些模型采用了不同的数据增强技术、学习率和优化器进行训练。数据来源于阿劳州立医院提供的私有数据集。最佳结果由ResNet3D模型实现，其平均精确率达到0.4583，AUC ROC评分为0.6214。