论文标题
验证医学图像翻译中的不确定性
Validating uncertainty in medical image translation
论文作者
论文摘要
医学图像越来越多地用作深度神经网络的输入，以产生有助于研究人员和临床医生的定量值。但是，标准的深神经网络不能提供这些定量值中不确定性的可靠度量。最近的工作表明，在训练和测试过程中使用辍学可以提供不确定性的估计。在这项工作中，我们调查使用辍学来估计CT-TO-MR图像翻译任务中的认知和差异不确定性。我们表明，捕获两种类型的不确定性，如定义，从而提供了对输出不确定性估计的信心。
Medical images are increasingly used as input to deep neural networks to produce quantitative values that aid researchers and clinicians. However, standard deep neural networks do not provide a reliable measure of uncertainty in those quantitative values. Recent work has shown that using dropout during training and testing can provide estimates of uncertainty. In this work, we investigate using dropout to estimate epistemic and aleatoric uncertainty in a CT-to-MR image translation task. We show that both types of uncertainty are captured, as defined, providing confidence in the output uncertainty estimates.
