一个系统的回顾医疗成像中的少数镜头学习
Eva Pachetti1, Sara Colantonio2
1Institute of Information Science and Technologies "Alessandro Faedo", National Research Council of Italy (ISTI-CNR), via Giuseppe Moruzzi 1, Pisa, 56124, PI, Italy; Department of Information Engineering, University of Pisa, via Girolamo Caruso 16, Pisa, 56122, PI, Italy.
Artificial intelligence in medicine
|August 23, 2024
概括
少数拍摄的学习解决了有限的注释医疗图像,以进行深度学习. 本综述概述了医疗图像分析中少数镜头学习的标准管道,强调元学习策略.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 深度学习模型需要大型注释数据集,在医学成像中很少.
- 短暂学习 (FSL) 技术减轻了数据稀缺性,提高了医疗图像分析的速度和稳定性.
研究的目的:
- 为医疗图像分析中FSL方法提供全面的概述.
- 为未来的研究建立一个标准的方法管道.
- 分析元学习在FSL中的作用,用于医学成像.
主要方法:
- 2018年至2023年间发表的80篇文章的系统审查.
- 偏差风险评估和使用的学习技术的提取.
- 关于临床任务和元学习方法的结果的统计分析.
主要成果:
- 在各个研究中确定了一个共同的方法管道.
- 进行了成像模式和模型稳定性评估技术的分析.
- 提供了对最先进的FSL方法的局限性和有希望的方法的见解.
结论:
- 特别是在元学习方面,FSL对医疗图像分析具有前景.
- 建议未来的研究,以弥合研究和临床实践之间的差距.
- 方法的标准化可以加强FSL在医疗保健中的发展和应用.
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