[论文] PR3DICTR: A modular AI framework for medical 3D image-based detection ...

论文概要

研究领域: CV 作者: Daniel C. MacRae, Luuk van der Hoek, Robert van der Wal 等 发布时间: 2026-04-03 arXiv: 2604.03203

中文摘要

三维医学图像数据和计算机辅助决策在医学领域正变得越来越重要。我们引入PR3DICTR：三维图像分类和标准化训练研究平台。PR3DICTR基于社区标准发行版构建，提供开放访问、灵活且便捷的预测模型开发框架，明确专注于使用三维医学图像数据进行分类。通过结合模块化设计原则和标准化，它旨在减轻开发负担同时保持可调整性。

原文摘要

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, hyp...

自动采集于 2026-04-06

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