PARADIM: A Platform to Support Research at the Interface of Data Science and Medical Imaging
This paper describes PARADIM, a digital infrastructure designed to support research at the interface of data science and medical imaging, with a focus on Research Data Management best practices. The platform is built from open-source components and rooted in the FAIR principles through strict compliance with the DICOM standard. It addresses key needs in data curation, governance, privacy, and scalable resource management. Supporting every stage of the data science discovery cycle, the platform offers robust functionalities for user identity and access management, data de-identification, storage, annotation, as well as model training and evaluation. Rich metadata are generated all along the research lifecycle to ensure the traceability and reproducibility of results. PARADIM hosts several medical image collections and allows the automation of large-scale, computationally intensive pipelines (e.g., automatic segmentation, dose calculations, AI model evaluation). The platform fills a gap at the interface of data science and medical imaging, where digital infrastructures are key in the development, evaluation, and deployment of innovative solutions in the real world.
Authors
- Michael Ingrisch (ORCID: https://orcid.org/0000-0003-0268-9078)
- Sébastien Jodogne (ORCID: https://orcid.org/0000-0001-6685-7398)
- Philippe Després (ORCID: https://orcid.org/0000-0002-4163-7353)
- Pierre-Luc Asselin
- Yannick Lemaréchal (ORCID: https://orcid.org/0000-0002-2494-2831)
- Johanna Topalis (ORCID: https://orcid.org/0000-0002-8623-8110)
- Samuel Ouellet (ORCID: https://orcid.org/0009-0009-8371-5071)
- Leyla Ebrahimpour (ORCID: https://orcid.org/0000-0001-7243-8296)
- François Pelletier (ORCID: https://orcid.org/0000-0001-9767-7058)
- Venkata Manem (ORCID: https://orcid.org/0000-0003-1427-2819)
- Balthasar Schachtner (ORCID: https://orcid.org/0000-0002-8712-3948)
- Gabriel Couture (ORCID: https://orcid.org/0000-0003-1832-6036)
- Katharina Jeblick (ORCID: https://orcid.org/0000-0002-8678-7152)
- Philippe Joubert (ORCID: https://orcid.org/0000-0001-7784-6387)
- Ronan Lefol (ORCID: https://orcid.org/0009-0002-4115-9635)
- J Bernard
Institutions
- Memorial Sloan Kettering Cancer Center (US)
- LMU Klinikum (DE)
- Institut universitaire de cardiologie et de pneumologie de Québec (CA)
- Centre hospitalier de l'Université Laval (CA)
- Université Laval (CA)
- Munich Center for Machine Learning
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Journal of Imaging Informatics in Medicine
- Published
- 2025-06-03
- DOI
- https://doi.org/10.1007/s10278-025-01554-y
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Canadian Cancer Society
- Deutscher Akademischer Austauschdienst
- Bundesministerium für Bildung und Forschung
- Bayerisches Staatsministerium für Wissenschaft und Kunst
- Natural Sciences and Engineering Research Council of Canada
- Fonds de Recherche du Québec - Santé