医学图像分析中不确定性量化的审查:概率和非概率方法
Ling Huang1, Su Ruan2, Yucheng Xing1
1Saw Swee Hock School of Public Health, National University of Singapore, Singapore.
Medical image analysis
|June 11, 2024
概括
不确定性量化方法对于验证机器学习医疗保健模型至关重要. 本综述探讨了可靠的医学图像分析的概率和非概率方法,增强了对临床实践的模型信任.
科学领域:
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
- 机器学习可靠性 机器学习可靠性
背景情况:
- 机器学习 (ML) 模型在医疗保健中表现有前途,但由于未经证实的可靠性,面临采用障碍.
- 量化ML模型预测的不确定性是提高其解释性和临床接受度的关键.
研究的目的:
- 为医学成像中的ML模型提供不确定性量化 (UQ) 方法的全面概述.
- 探索概率和非概率的UQ方法来进行整体调查.
- 帮助研究人员理解UQ用于医学图像分析的ML模型.
主要方法:
- 对医学成像中的ML模型应用的UQ技术的系统审查.
- 概率和非概率的UQ方法的分类和分析.
- 在特定的医学成像应用中检查UQ协议.
主要成果:
- 在医学成像中确定了一系列用于ML的UQ方法.
- 强调了UQ对于提高ML模型的信任性和可解释性的重要性.
- 讨论了UQ在医学图像分析中的挑战和具体协议.
结论:
- UQ对于弥合高性能ML模型和其临床集成之间的差距至关重要.
- 本综述提供了UQ的广泛视角,包括新的非概率学方法.
- 概述了UQ对医学成像ML的未来研究方向.
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