The use of deep learning approaches for image reconstruction is of contemporary interest in radiology, especially for approaches that solve inverse problems associated with imaging. In deployment, these models may be exposed to input distributions that are widely shifted from training data, due in part to data biases or drifts. We propose a metric based on local Lipschitz determined from a single trained model that can be used to estimate the model uncertainty for image reconstructions. We demonstrate a monotonic relationship between the local Lipschitz value and Mean Absolute Error and show that this method can be used to provide a threshold that determines whether a given DL reconstruction approach was well suited to the task. Our uncertainty estimation method can be used to identify out-of-distribution test samples, relate information regarding epistemic uncertainties, and guide proper data augmentation. Quantifying uncertainty of learned reconstruction approaches is especially pertinent to the medical domain where reconstructed images must remain diagnostically accurate.

翻译：深度学习在图像重建中的应用在放射学领域备受关注，尤其是解决成像逆问题的方法。在实际部署中，由于数据偏差或漂移等因素，这些模型可能面临与训练数据分布显著不同的输入分布。我们提出了一种基于单训练模型局部Lipschitz值的度量方法，可用于估计图像重建的模型不确定性。研究表明，局部Lipschitz值与平均绝对误差之间存在单调关系，且该方法能提供阈值以判断特定深度学习重建方法是否适用于当前任务。我们的不确定性估计方法可用于识别分布外测试样本、获取认知不确定性相关信息，并指导合理的数据增强。在医学领域，重建图像必须保持诊断准确性，因此量化学习型重建方法的不确定性尤为重要。