A Review of Uncertainty Estimation and its Application in Medical Imaging

TL;DR

This paper reviews the importance of uncertainty estimation in deep learning for medical imaging, highlighting types, methods, recent advances, challenges, and future directions to improve AI reliability in healthcare.

Contribution

It provides a comprehensive overview of uncertainty types, estimation techniques, recent model developments, and discusses future challenges in applying uncertainty estimation to medical imaging.

Findings

Uncertainty estimation enhances trustworthiness of AI in medical diagnosis.

Recent deep learning models effectively incorporate uncertainty measures.

Identifies key challenges and future research directions in the field.

Abstract

The use of AI systems in healthcare for the early screening of diseases is of great clinical importance. Deep learning has shown great promise in medical imaging, but the reliability and trustworthiness of AI systems limit their deployment in real clinical scenes, where patient safety is at stake. Uncertainty estimation plays a pivotal role in producing a confidence evaluation along with the prediction of the deep model. This is particularly important in medical imaging, where the uncertainty in the model's predictions can be used to identify areas of concern or to provide additional information to the clinician. In this paper, we review the various types of uncertainty in deep learning, including aleatoric uncertainty and epistemic uncertainty. We further discuss how they can be estimated in medical imaging. More importantly, we review recent advances in deep learning models that incorporate uncertainty estimation in medical imaging. Finally, we discuss the challenges and future directions in uncertainty estimation in deep learning for medical imaging. We hope this review will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of uncertainty estimation models in medical imaging.