Multi-modal dataset creation for federated learning with DICOM-structured reports.

ABSTRACT: Purpose Federated training is often challenging on heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging multi-modal learning paradigms, where dataset harmonization including a uniform data representation and filtering options are of paramount importance.Methods DICOM-structured reports enable the standardized linkage of arbitrary information beyond the imaging domain and can be used within Python deep learning pipelines with highdicom. Building on this, we developed an open platform for data integration with interactive filtering capabilities, thereby simplifying the process of creation of patient cohorts over several sites with consistent multi

SUBMITTER: Tolle M 

PROVIDER: S-EPMC11929732 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature