PARADIM: A Platform to Support Research at the Interface of Data Science and Medical Imaging.
Authors
Affiliations (10)
Affiliations (10)
- Département de physique, de génie physique et d'optique, Université Laval, Québec, Québec, Canada.
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec-Université Laval, Québec, Québec, Canada.
- Centre de recherche du CHU de Québec-Université Laval, Québec, Québec, Canada.
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
- Munich Center for Machine Learning (MCML), Munich, Germany.
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium.
- Département de physique, de génie physique et d'optique, Université Laval, Québec, Québec, Canada. [email protected].
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec-Université Laval, Québec, Québec, Canada. [email protected].
- Centre de recherche du CHU de Québec-Université Laval, Québec, Québec, Canada. [email protected].
Abstract
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.