Clinician-driven automated data preprocessing in nuclear medicine AI environments
The study demonstrated the added value of RST in predictive performance of oncology-specific ML models, hence, serving as proof of concept of a more inclusive clinician-driven DP process in future studies.
Authors
- Zsombor Ritter (ORCID: https://orcid.org/0000-0002-2898-1571)
- Denis Krajnc (ORCID: https://orcid.org/0000-0003-1786-4297)
- Clemens P. Spielvogel (ORCID: https://orcid.org/0000-0002-4409-8324)
- Hussain Alizadeh (ORCID: https://orcid.org/0000-0002-4609-487X)
- László Papp (ORCID: https://orcid.org/0000-0002-9049-9989)
- Marcus Hacker (ORCID: https://orcid.org/0000-0002-4222-4083)
- Boglarka Ecsedi (ORCID: https://orcid.org/0000-0002-3592-8306)
Institutions
- Georgia Institute of Technology (US)
- Medical University of Vienna (AT)
- University of Pecs (HU)
Publication Details
- Journal
- European Journal of Nuclear Medicine and Molecular Imaging
- Published
- 2025-03-07
- DOI
- https://doi.org/10.1007/s00259-025-07183-5
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Medizinische Universität Wien