The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions reported in the literature. A predominant factor hindering widespread adoption pertains to an insufficiency of evidence affirming the reliability of the aforementioned models. Recently, uncertainty quantification methods have been proposed as a potential solution to quantify the reliability of machine learning models and thus increase the interpretability and acceptability of the result. In this review, we offer a comprehensive overview of prevailing methods proposed to quantify uncertainty inherent in machine learning models developed for various medical image tasks. Contrary to earlier reviews that exclusively focused on probabilistic methods, this review also explores non-probabilistic approaches, thereby furnishing a more holistic survey of research pertaining to uncertainty quantification for machine learning models. Analysis of medical images with the summary and discussion on medical applications and the corresponding uncertainty evaluation protocols are presented, which focus on the specific challenges of uncertainty in medical image analysis. We also highlight some potential future research work at the end. Generally, this review aims to allow researchers from both clinical and technical backgrounds to gain a quick and yet in-depth understanding of the research in uncertainty quantification for medical image analysis machine learning models.

综合机器学习医疗模型在临床实践中的应用仍然不理想，缺乏证据证明其可靠性进而限制了其广泛应用。本文综述了用于各种医学图像任务中开发的机器学习模型的不确定性量化方法，包括概率和非概率方法，以全面调研与不确定性量化相关的研究。该综述对医学图像的分析和医学应用以及相应的不确定性评估协议进行了讨论，并强调了未来的研究方向。整体上，本综述旨在帮助临床和技术领域的研究人员快速而深入地了解医学图像分析机器学习模型中的不确定性量化研究。

医学图像分析中的不确定性量化综述：概率和非概率方法