• Computer Science > Distributed, Parallel, and Cluster Computing [Submitted on 3 Oct 2025 (v1), last revised 19 Feb 2026 (this version, v2)] Title:PyRadiomics-cuda: 3D features extraction from medical images for HPC using GPU acceleration View PDF HTML (experimental)Abstract: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. • Submission history From: Krzysztof Kaczmarski [view email][v1] Fri, 3 Oct 2025 11:00:31 UTC (115 KB) [v2] Thu, 19 Feb 2026 15:56:26 UTC (119 KB) References & Citations export BibTeX citation Loading…
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- Computer Science > Distributed, Parallel, and Cluster Computing [Submitted on 3 Oct 2025 (v1), last revised 19 Feb 2026 (this version, v2)] Title:PyRadiomics-cuda: 3D features extraction from medical images for HPC using GPU acceleration View PDF HTML (experimental)Abstract: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
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