Three-dimensional medical image data and computer-aided decision making, particularly using deep learning, are becoming increasingly important in the medical field. To aid in these developments we introduce PR3DICTR: Platform for Research in 3D Image Classification and sTandardised tRaining. Built using community-standard distributions (PyTorch and MONAI), PR3DICTR provides an open-access, flexible and convenient framework for prediction model development, with an explicit focus on classification using three-dimensional medical image data. By combining modular design principles and standardization, it aims to alleviate developmental burden whilst retaining adjustability. It provides users with a wealth of pre-established functionality, for instance in model architecture design options, hyper-parameter solutions and training methodologies, but still gives users the opportunity and freedom to ``plug in'' their own solutions or modules. PR3DICTR can be applied to any binary or event-based three-dimensional classification task and can work with as little as two lines of code.

翻译：三维医学图像数据与计算机辅助决策，特别是基于深度学习的方法，在医学领域日益重要。为支持这些发展，我们提出PR3DICTR：三维图像分类与标准化训练研究平台。该平台基于社区标准发行版（PyTorch和MONAI）构建，提供开放获取、灵活便捷的预测模型开发框架，明确聚焦于三维医学图像数据的分类应用。通过融合模块化设计原则与标准化流程，PR3DICTR旨在减轻开发负担的同时保持可调性。平台为用户提供丰富的预置功能，例如模型架构设计选项、超参数解决方案及训练方法，但同时也赋予用户“插入”自有解决方案或模块的灵活性与自由度。PR3DICTR可应用于任何二元或基于事件的三维分类任务，且仅需两行代码即可运行。