Reference Manual of MEDiml
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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BibTeX
@misc{loutfi2026reference,
title = {{Reference Manual of MEDiml}},
author = {Mahdi Ait Lhaj Loutfi and Simon Provost and Élodie Gillard and Moustafa Amine Bezzahi and Alexander Chum and Ben Coull-Nveu and Duran Juan and Mariem Kallel and Hakima Laribi and Oliver Lefebvre and Nicolas Longchamps and Thanh-Tri Nguyen and Steven Ravary and Ouael Nedjem Eddine Sahbi and Jérémy Dana and Martin Vallières},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.29666},
eprint = {2610.29666},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.29666},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Reference Manual of MEDiml
AU - Mahdi Ait Lhaj Loutfi
AU - Simon Provost
AU - Élodie Gillard
AU - Moustafa Amine Bezzahi
AU - Alexander Chum
AU - Ben Coull-Nveu
AU - Duran Juan
AU - Mariem Kallel
AU - Hakima Laribi
AU - Oliver Lefebvre
AU - Nicolas Longchamps
AU - Thanh-Tri Nguyen
AU - Steven Ravary
AU - Ouael Nedjem Eddine Sahbi
AU - Jérémy Dana
AU - Martin Vallières
PY - 2026
DA - 2026/10/21
PB - arXiv
DO - 10.48550/arxiv.2610.29666
UR - https://arxiv.org/abs/2610.29666
AB - 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.
ER -