Uncertainty quantification in inverse medical imaging tasks with deep
learning has received little attention. However, deep models trained on large
data sets tend to hallucinate and create artifacts in the reconstructed output
that are not anatomically present. We use a randomly initialized convolutional
network as parameterization of the reconstructed image and perform gradient
descent to match the observation, which is known as deep image prior. In this
case, the reconstruction does not suffer from hallucinations as no prior
training is performed. We extend this to a Bayesian approach with Monte Carlo
dropout to quantify both aleatoric and epistemic uncertainty. The presented
method is evaluated on the task of denoising different medical imaging
modalities. The experimental results show that our approach yields
well-calibrated uncertainty. That is, the predictive uncertainty correlates
with the predictive error. This allows for reliable uncertainty estimates and
can tackle the problem of hallucinations and artifacts in inverse medical
imaging tasks.

通过随机初始化卷积网络对图像进行参数化并执行梯度下降，以匹配观察结果的方法来量化反向医学成像任务中的不确定性，扩展到带有蒙特卡罗失活的贝叶斯方法，实现对预测不确定性的可靠估计，解决了大型数据集上深度神经网络出现幻觉和制品所带来的问题。