用GPU加速医学影像三维特征提取，处理速度大幅提升。
PyRadiomics-cuda: 3D features extraction from medical images for HPC using GPU acceleration
- 将几何计算迁移至GPU，显著提速3D特征提取
- 在集群、预算机和家用设备上均实现高效运行
- 兼容原PyRadiomics接口，无需改代码直接接入
PyRadiomics-cuda是PyRadiomics库的GPU加速扩展，旨在解决从医学影像中提取三维形状特征时的计算瓶颈。通过将关键几何计算任务卸载到GPU硬件，该系统大幅缩短了大型体数据集的处理时间。系统保持与原始PyRadiomics API完全兼容，可无缝集成到现有AI工作流中，无需修改代码。这种透明化加速支持高效、可扩展的放射组学分析，满足高通量AI流程对快速特征提取的需求。在典型计算集群、预算级设备及家用设备上的测试验证了其在各类场景下的实用性。
原文摘要 · Abstract (English)
PyRadiomics-cuda is a GPU-accelerated extension of the PyRadiomics library, designed to address the computational challenges of extracting three-dimensional shape features from medical images. By offloading key geometric computations to GPU hardware it dramatically reduces processing times for large volumetric datasets. The system maintains full compatibility with the original PyRadiomics API, enabling seamless integration into existing AI workflows without code modifications. This transparent acceleration facilitates efficient, scalable radiomics analysis, supporting rapid feature extraction essential for high-throughput AI pipeline. Tests performed on a typical computational cluster, budget and home devices prove usefulness in all scenarios.
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