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.

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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
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article

Deep evidential learning for radiotherapy dose prediction

Hai Siong Tan, Rafe McBeth, Kuancheng Wang
5 citations
Computers in Biology and Medicine
Advanced Radiotherapy Techniques
3.04
article

Deep evidential learning for radiotherapy dose prediction

Hai Siong Tan, Rafe McBeth, Kuancheng Wang
article en
5 citations

Abstract

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.

Computers in Biology and MedicineVol. 182
Georgia Institute of Technology (US), Artificial Intelligence in Medicine (Canada) (CA), University of Pennsylvania (US)
Openalex Percentile: Top 9%
Advanced Radiotherapy Techniques
3.04
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