Newest papers

Controlling Variability in~Neuroimaging Analysis Pipelines with~Software Product Lines

Gauthier Le Bartz Lyan, Paul Temple, Boris Clénet, Mathieu Acher · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29821 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

磁共振成像(MRI)是一种用于医学诊断的获取技术。它主要用于结构和功能的MRI(或fMRI)图像分析。MRI数据是众所周知的复杂和噪音,所以管道被创建以提取和增强获取图像中的相关信息。NARPS研究强调了独立团队在试图回答大脑功能相同的研究问题时可能出现的不同管道的显著变异性。具有不同实现相同任务的实施是软件产品线(SPLs)的典型游乐场,这可能对神经图像学家来说是远离视野的。因此,提出这些不同管道中的SPL回归到典型的逆转工程,具有其常见问题困难。在本文中,我们倡导采用SPL原则作为改善神经观测的可再生性和可

Machine translation (Meta M2M100); the English original follows.

Abstract

Magnetic resonance imaging (MRI) is an acquisition technique used for medical diagnosis. It is notably used for structural and functional MRI (or fMRI) image analysis. MRI data is known to be complex and noisy, so pipelines are created to extract and enhance relevant information in retrieved images. The NARPS study highlighted the significant variability that can emerge from different pipelines set up by independent teams while trying to answer the same research question about brain functioning. Having different implementations realizing the same task is a typical playground for software product lines (SPLs), which may be out of the sight of neuroimage practitioners. Thus, proposing an SPL out of these different pipelines falls back on a typical reverse-engineering problem with its usual difficulties. In this document, we advocate for the conceptual adoption of SPL principles as a promising direction to improve reproducibility and consistency in neuroimaging research.

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Topics: Pipeline transport, Consistency (knowledge bases), Computer science, Product (mathematics), Software, Task (project management)

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