论文标题
医疗图像中的不确定性估计与贝叶斯深图像先验
Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior
论文作者
论文摘要
具有深度学习的反医学成像任务中的不确定性量化很少受到关注。但是，对大型数据集训练的深层模型往往会幻觉，并在不存在解剖的重建输出中创建工件。我们使用随机初始化的卷积网络作为重建图像的参数化，并执行梯度下降以匹配观察值，这被称为“深图像先验”。在这种情况下，由于未进行先前的培训，因此重建不会遭受幻觉的影响。我们将其扩展到使用Monte Carlo辍学的贝叶斯方法，以量化核心和认知不确定性。对提出的方法进行了评估，以剥夺不同医学成像方式的任务。实验结果表明，我们的方法产生了良好的不确定性。也就是说，预测不确定性与预测误差相关。这可以进行可靠的不确定性估计，并可以解决逆医学成像任务中的幻觉和工件问题。
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.
