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Reference Manual of MEDiml

Mahdi Ait Lhaj Loutfi, Simon Provost, Elodie Gillard, Moustafa Amine Bezzahi, Alexander Chum, Ben Coull-Nveu, Duran Juan, Mariem Kallel, Hakima Laribi, Oliver Lefebvre, Nicolas Longchamps, Thanh-Tri Nguyen, Steven Ravary, Ouael Nedjem Eddine Sahbi, Jérémy Dana, Martin Vallières · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29666 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

这些功能可以作为癌症诊断,预测和分类的候选图像生物标志物(例如,肿瘤形状,强度分布和结构)从医学图像中提取高输出能力来描绘组织和揭示人类眼睛看不见的复杂疾病模式。然而,从医疗图像中提取成千上万的特征使得难以识别最具信息性的特定临床任务,同时实施一个端到端的复杂工作流程,可能需要编程专业知识,限制了临床研究人员的可访问性。因此,我们提出了Mediml,一种开源、符合标准的无线电分类平台,通过补充图形和程序过滤工作界面来识别功能提取,预测模型和解释功能。Mediml结合了现有的Python解读包(MED-lpkg)

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

Abstract

Radiomics is the high-throughput extraction of quantitative features (e.g., tumor shape, intensity distributions, and texture) from medical images to characterize tissues and uncover disease patterns invisible to the human eye. These features can serve as candidate imaging biomarkers for cancer diagnosis, prognosis, and classification. However, extracting thousands of features makes it difficult to identify the most informative ones for a given clinical task, while implementing an end-to-end workflow can require programming expertise that limits accessibility for clinical researchers. Hence, we propose MEDiml, an open-source, standards-compliant radiomics platform connecting feature extraction, predictive modeling, and interpretation through complementary graphical and programmatic interfaces. MEDiml combines a Python package (MEDiml-pkg) with a graphical desktop application (MEDiml-app) that enables researchers to conduct experiments without writing code. Both interfaces use the same computational implementation, and code generation exports graphically configured workflows as executable Python code, facilitating reproducibility and collaboration between clinical researchers and computational scientists. A typical MEDiml workflow includes image preprocessing, radiomic feature extraction, feature set reduction, model training and evaluation, and interpretation using feature importance. Feature sets can be grouped into levels of radiomic complexity (morphological, intensity-based, textural, and filter-based) to evaluate whether more complex features provide additional predictive value. This reference manual documents the radiomics concepts, workflow, software components, architecture, and installation, compares MEDiml with existing tools, and demonstrates its complexity-driven workflow on five oncological imaging cohorts comprising 2,104 patients. Resources: www.mediml.app.

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Topics: Workflow, Python (programming language), Computer science, Executable, Feature (linguistics), Software

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