PR3DICTR让医生用最少代码完成3D医学影像分类与预后预测。
PR3DICTR: A modular AI framework for medical 3D image-based detection and outcome prediction
- 模块化设计，支持自定义组件快速接入现有流程。
- 只需两行代码即可完成任意3D医学图像的分类任务。
- 开源易用，适合医疗AI研究者快速搭建模型。
三维医学影像数据与基于深度学习的计算机辅助决策正日益重要。为此，我们提出PR3DICTR：用于3D图像分类与标准化训练的研究平台。基于社区标准工具（PyTorch与MONAI），该框架提供开放、灵活且便捷的预测模型开发环境，专注于三维医学影像分类。通过模块化设计与标准化机制，降低开发负担的同时保持可调性。内置丰富功能，如模型架构选项、超参数配置与训练方法，用户也可自由插入自定义模块。PR3DICTR适用于任意二分类或事件类3D分类任务，仅需两行代码即可运行。
原文摘要 · Abstract (English)
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.
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