Deep Evidential Learning for Radiotherapy Dose Prediction

TL;DR

This paper introduces Deep Evidential Learning for radiotherapy dose prediction, providing effective uncertainty estimates that correlate with prediction errors and enhance model robustness in medical imaging applications.

Contribution

It adapts and reformulates the Deep Evidential Learning framework for radiotherapy dose prediction, demonstrating improved uncertainty calibration and robustness over traditional methods.

Findings

Epistemic uncertainty correlates strongly with prediction errors.

Uncertainty estimates show more linear error thresholds compared to other methods.

The model's aleatoric uncertainty responds appropriately to added Gaussian noise.

Abstract

In this work, we present a novel application of an uncertainty-quantification framework called Deep Evidential Learning in the domain of radiotherapy dose prediction. Using medical images of the Open Knowledge-Based Planning Challenge dataset, we found that this model can be effectively harnessed to yield uncertainty estimates that inherited correlations with prediction errors upon completion of network training. This was achieved only after reformulating the original loss function for a stable implementation. We found that (i)epistemic uncertainty was highly correlated with prediction errors, with various association indices comparable or stronger than those for Monte-Carlo Dropout and Deep Ensemble methods, (ii)the median error varied with uncertainty threshold much more linearly for epistemic uncertainty in Deep Evidential Learning relative to these other two conventional frameworks, indicative of a more uniformly calibrated sensitivity to model errors, (iii)relative to epistemic uncertainty, aleatoric uncertainty demonstrated a more significant shift in its distribution in response to Gaussian noise added to CT intensity, compatible with its interpretation as reflecting data noise. 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. Towards enhancing its clinical relevance, we demonstrate how we can use such a model to construct the predicted Dose-Volume-Histograms' confidence intervals.