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.

在放射治疗剂量预测领域，我们应用了一种名为深度证据学习的不确定性量化框架，通过使用 Open Knowledge-Based Planning Challenge 数据集的医学影像，我们发现该模型可以有效地产生与预测误差相关的不确定性估计，在网络训练完成后仅经过重新构建原始损失函数以实现稳定实现。实验结果表明，深度证据学习相对于 Monte-Carlo Dropout 和 Deep Ensemble 方法，能够更线性地根据不确定性阈值变化的中位误差，与模型误差均匀校准的敏感性。同时，我们发现对于加入高斯噪声的 CT 强度，与表征数据噪声的 aleatoric 不确定性表现出更显著的分布变化。总的来说，我们的研究结果表明深度证据学习是一种有前景的方法，可以为放射治疗剂量预测的深度学习模型提供统计稳健性，我们还展示了如何使用这种模型构建预测的剂量体积直方图的置信区间。

深度证据学习的剂量预测

Deep Evidential Learning for Dose Prediction

As a promising fashion for visual localization, scene coordinate regression
(SCR) has seen tremendous progress in the past decade. Most recent methods
usually adopt neural networks to learn the mapping from image pixels to 3D
scene coordinates, which requires a vast amount of annotated training data. We
propose to leverage Neural Radiance Fields (NeRF) to generate training samples
for SCR. Despite NeRF's efficiency in rendering, many of the rendered data are
polluted by artifacts or only contain minimal information gain, which can
hinder the regression accuracy or bring unnecessary computational costs with
redundant data. These challenges are addressed in three folds in this paper:
(1) A NeRF is designed to separately predict uncertainties for the rendered
color and depth images, which reveal data reliability at the pixel level. (2)
SCR is formulated as deep evidential learning with epistemic uncertainty, which
is used to evaluate information gain and scene coordinate quality. (3) Based on
the three arts of uncertainties, a novel view selection policy is formed that
significantly improves data efficiency. Experiments on public datasets
demonstrate that our method could select the samples that bring the most
information gain and promote the performance with the highest efficiency.

本文提出了一种使用神经辐射场（NeRF）生成训练样本的场景坐标回归（SCR）方法，并且通过设计 NeRF 来预测渲染数据的不确定性，在像素级别揭示数据的可靠性；将 SCR 公式化为带有认识不确定性的深度证据学习，用来评估信息获取与场景坐标质量；基于不确定性的三个方面，形成了一种新的视角选择策略，极大地提高了数据效率。实验结果表明，我们的方法能够选择带来最大信息增益的样本，并以最高效率提高性能。