Related papers: A Review of Uncertainty Estimation and its Application in Medical Imaging

A Review of Uncertainty Estimation and its Application in Medical Imaging

URL: http://arxiv.org/abs/2302.08119v3
Date: Tue, 16 May 2023 03:41:29 GMT
Title: A Review of Uncertainty Estimation and its Application in Medical Imaging
Authors: Ke Zou and Zhihao Chen and Xuedong Yuan and Xiaojing Shen and Meng Wang and Huazhu Fu
Abstract summary: 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.
Score: 32.860577735207094
License: http://creativecommons.org/licenses/by/4.0/
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.

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