RELICT-NI: Replica Detection in Synthetic Neuroimaging—A Study on Noncontrast CT and Time-of-Flight MRA
Our developed replica detection framework provides an important step towards standardized and rigorous validation practices of generative models in neuroimaging. Our method promotes the secure sharing of neuroimaging data and facilitates the development of robust deep learning models.
Authors
- Fujimaro Ishida (ORCID: https://orcid.org/0000-0001-6324-027X)
- Adam Hilbert (ORCID: https://orcid.org/0000-0003-3447-5453)
- Orhun Utku Aydin (ORCID: https://orcid.org/0000-0002-8808-7651)
- Alexander Koch (ORCID: https://orcid.org/0000-0002-3510-9669)
- Jana Rieger (ORCID: https://orcid.org/0000-0002-3906-316X)
- Satoru Tanioka (ORCID: https://orcid.org/0000-0002-4678-6163)
- Dietmar Frey (ORCID: https://orcid.org/0000-0001-5407-2331)
- Felix Lohrke
Institutions
- Mie University (JP)
- Humboldt-Universität zu Berlin (DE)
- Mie Chuo Medical Center (JP)
- Charité - Universitätsmedizin Berlin (DE)
Publication Details
- Journal
- Neuroinformatics
- Published
- 2025-11-10
- DOI
- https://doi.org/10.1007/s12021-025-09745-2
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- University of Southern California
- European Commission
- Bundesministerium für Bildung und Forschung
- Berlin Institute of Health
- University of California, Los Angeles
- University of North Carolina at Chapel Hill
- HORIZON EUROPE Framework Programme