论文标题
学习预测MRI重建的错误
Learning to Predict Error for MRI Reconstruction
论文作者
论文摘要
在医疗保健应用中，已使用预测不确定性来评估预测准确性。在本文中，我们证明，通过当前方法估计的预测不确定性通过将后者分解为随机和系统误差而与预测误差高度相关，并且表明前者等同于随机误差的方差。此外，我们观察到，当前方法通过修改模型和训练损失以共同估算目标和不确定性来不必要地损害性能。我们表明，在没有修改的情况下分别估算它们会提高性能。在此之后，我们提出了一种新的方法，该方法以两个步骤估算预测误差的目标标签和幅度。我们在大规模的MRI重建任务上证明了这种方法，并且比最新的不确定性估计方法获得了明显更好的结果。
In healthcare applications, predictive uncertainty has been used to assess predictive accuracy. In this paper, we demonstrate that predictive uncertainty estimated by the current methods does not highly correlate with prediction error by decomposing the latter into random and systematic errors, and showing that the former is equivalent to the variance of the random error. In addition, we observe that current methods unnecessarily compromise performance by modifying the model and training loss to estimate the target and uncertainty jointly. We show that estimating them separately without modifications improves performance. Following this, we propose a novel method that estimates the target labels and magnitude of the prediction error in two steps. We demonstrate this method on a large-scale MRI reconstruction task, and achieve significantly better results than the state-of-the-art uncertainty estimation methods.
