用更少的时间学习:在不同的监督水平下,对医学成像中的深度学习进行了调查
Suruchi Kumari1, Pravendra Singh1
1Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, India.
Artificial intelligence in medicine
|February 17, 2026
概括
医疗图像分析的深度学习面临着数据限制. 本综述对使用不同监督水平 (例如,半监督学习) 的方法进行了分类,以克服数据稀缺,帮助未来的研究.
科学领域:
- 医学图像分析 医学图像分析
- 深度学习 (Deep Learning) 是一种深度学习.
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 深度学习已经推进了医学图像分析.
- 进步受到大型,注释良好的数据集稀缺性的限制.
- 使用不同监督级别的替代方法至关重要.
研究的目的:
- 系统地审查医疗图像分析的深度学习方法.
- 根据监督级别对方法进行分类 (没有,不准确,不完整,不准确,有限).
- 探索数据集和未来的研究方向.
主要方法:
- 根据监督级别对深度学习方法的分类.
- 监督类型的分类 (例如,多实例学习,半监督学习).
- 数据集的系统总结和未来的研究.
主要成果:
- 建立了基于监督的医疗成像深度学习方法分类的框架.
- 详细介绍了每个监督级别内的各种子类别.
- 针对不同类型的监督,确定常用的数据集.
结论:
- 通过多样化的监督来解决数据稀缺问题是医学成像深度学习的关键.
- 该审查提供了当前方法的结构化概述.
- 强调需要在这个领域继续进行研究.
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