Estimating Uncertainty of Deep Learning for Tomographic Image Reconstruction Through Local Lipschitz

As deep learning approaches for image reconstruction become increasingly used in the radiological space, strategies to estimate reconstruction uncertainties become critically important to ensure images remain diagnostic. We estimate reconstruction uncertainty through calculation of the Local Lipschitz value, demonstrate a monotonic relationship between the Local Lipschitz and Mean Absolute Error, and show how a threshold can determine whether the deep learning technique was accurate or if an alternative technique should be employed. We also show how our technique can be used to identify out-of-distribution test images and outperforms baseline metrics, i.e. deep ensemble and Monte-Carlo dropout.