Explainability in medical image reconstruction with learning to optimize

Abstract

Learning to Optimize (L2O) is an emerging research area in machine learning, focusing on designing and training optimization algorithms that can learn to improve their own performance through experience. Each inference solves a data-driven optimization problem. L2O models are designed to be easy to deploy, incorporate prior knowledge and ensure correctness, such as satisfaction of constraints. This paper applies L2O with the combination of certificates, achieving a higher level of explainability for AI decisions than previous Explainable AI (XAI) methods on two low-dose CT image reconstruction datasets, LoDoPab and Ellipses. The paper also introduces a method to reduce the number of parameters and training time of the model while maintaining the same performance and ensuring the constraints conditions.