Uncertainty-aware deep learning for segmentation of primary tumor and pathologic lymph nodes in oropharyngeal cancer: Insights from a multi-center cohort
Significant accuracy versus uncertainty calibration was achieved for TPMs in both internal and external test sets, indicating the potential use of quantified uncertainty from TPMs to identify cases with lower GTVp and GTVln segmentation accuracy, independently of the dataset.
Authors
- Alessia de Biase
- Johannes A. Langendijk (ORCID: https://orcid.org/0000-0003-1083-372X)
- Nanna M. Sijtsema
- Peter M. A. van Ooijen (ORCID: https://orcid.org/0000-0002-8995-1210)
- Lisanne V. van Dijk (ORCID: https://orcid.org/0000-0002-9515-5616)
- Roel J.H.M. Steenbakkers
Institutions
- University Medical Center Groningen (NL)
Publication Details
- Journal
- Computerized Medical Imaging and Graphics
- Published
- 2025-03-27
- DOI
- https://doi.org/10.1016/j.compmedimag.2025.102535
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- Hanarth Fonds