PyRadiomics-cuda: a GPU-accelerated 3D features extraction from medical images within PyRadiomics

• URL: http://arxiv.org/abs/2510.02894v1
• Date: Fri, 03 Oct 2025 11:00:31 GMT
• Title: PyRadiomics-cuda: a GPU-accelerated 3D features extraction from medical images within PyRadiomics
• Authors: Jakub Lisowski, Piotr Tyrakowski, Szymon Zyguła, Krzysztof Kaczmarski,
• Abstract summary: PyRadiomics-cuda is a GPU-accelerated extension of the PyRadiomics library.<n>It addresses the computational challenges of extracting shape features from medical images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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. PyRadiomics-cuda is implemented in Python and C/CUDA and is freely available under the BSD license at https://github.com/mis-wut/pyradiomics-CUDA Additionally PyRadiomics-cuda test suite is available at https://github.com/mis-wut/pyradiomics-cuda-data-gen. It provides detailed handbook and sample scripts suited for different kinds of workflows plus detailed installation instructions. The dataset used for testing is available at Kaggle https://www.kaggle.com/datasets/sabahesaraki/kidney-tumor-segmentation-challengekits-19

Related papers

• scipy.spatial.transform: Differentiable Framework-Agnostic 3D Transformations in Python [49.015684860172975]
  Three-dimensional rigid-body transforms, i.e. rotations and translations, are central to modern differentiable machine learning pipelines.<n> SciPy's spatial$.$diff module is a rigorously tested Python implementation.<n>We present a complete overhaul of SciPy's spatial$.$transform functionality that makes it compatible with any array library implementing the Python array API.
  arXiv  Detail & Related papers  (2025-11-22T18:52:34Z)
• PyPulse: A Python Library for Biosignal Imputation [58.35269251730328]
  We introduce PyPulse, a Python package for imputation of biosignals in both clinical and wearable sensor settings.<n>PyPulse's framework provides a modular and extendable framework with high ease-of-use for a broad userbase, including non-machine-learning bioresearchers.<n>We released PyPulse under the MIT License on Github and PyPI.
  arXiv  Detail & Related papers  (2024-12-09T11:00:55Z)
• UncertaintyPlayground: A Fast and Simplified Python Library for Uncertainty Estimation [0.0]
  UncertaintyPlayground is a Python library built on PyTorch and GPyTorch for uncertainty estimation in supervised learning tasks. The library offers fast training for Gaussian and multi-modal outcome distributions. It can visualize the prediction intervals of one or more instances.
  arXiv  Detail & Related papers  (2023-10-23T18:36:54Z)
• Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow [50.691232400959656]
  Trieste is an open-source Python package for Bayesian optimization and active learning. Our library enables the plug-and-play of popular models within sequential decision-making loops.
  arXiv  Detail & Related papers  (2023-02-16T17:21:49Z)
• Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures [99.70485761868193]
  We present Torchhd, a high-performance open source Python library for HD/VSA. Torchhd seeks to make HD/VSA more accessible and serves as an efficient foundation for further research and application development.
  arXiv  Detail & Related papers  (2022-05-18T20:34:25Z)
• PyGOD: A Python Library for Graph Outlier Detection [56.33769221859135]
  PyGOD is an open-source library for detecting outliers in graph data. It supports a wide array of leading graph-based methods for outlier detection. PyGOD is released under a BSD 2-Clause license at https://pygod.org and at the Python Package Index (PyPI)
  arXiv  Detail & Related papers  (2022-04-26T06:15:21Z)
• PySAD: A Streaming Anomaly Detection Framework in Python [0.0]
  Streaming anomaly detection requires algorithms that operate under strict constraints.<n>We present PySAD, a comprehensive Python framework addressing these challenges through a unified architecture.
  arXiv  Detail & Related papers  (2020-09-05T17:41:37Z)
• TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning [68.8204255655161]
  We present TorchIO, an open-source Python library to enable efficient loading, preprocessing, augmentation and patch-based sampling of medical images for deep learning. TorchIO follows the style of PyTorch and integrates standard medical image processing libraries to efficiently process images during training of neural networks. It includes a command-line interface which allows users to apply transforms to image files without using Python.
  arXiv  Detail & Related papers  (2020-03-09T13:36:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.