Deep evidential learning for radiotherapy dose prediction
Collectively, our results suggest that Deep Evidential Learning is a promising approach that can endow deep-learning models in radiotherapy dose prediction with statistical robustness. We have also demonstrated how this framework leads to uncertainty heatmaps that correlate strongly with model errors, and how it can be used to equip the predicted Dose-Volume-Histograms with confidence intervals.
Authors
- Hai Siong Tan (ORCID: https://orcid.org/0000-0002-6926-1239)
- Rafe McBeth (ORCID: https://orcid.org/0000-0002-0924-6010)
- Kuancheng Wang
Institutions
- Georgia Institute of Technology (US)
- Artificial Intelligence in Medicine (Canada) (CA)
- University of Pennsylvania (US)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2024-09-23
- DOI
- https://doi.org/10.1016/j.compbiomed.2024.109172
- Citations
- 5
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 3.04