医学图像分析的自我监督学习框架应用:一篇综述和摘要
Xiangrui Zeng1, Nibras Abdullah2, Putra Sumari3
1School of Computer Sciences, Universiti Sains Malaysia, USM, 11800, Pulau Pinang, Malaysia. xavierzeng@student.usm.my.
Biomedical engineering online
|October 28, 2024
概括
自主监督学习通过使用未标记的数据来减少医疗AI的手动注释. 这篇评论检查了CT,MRI和X射线成像方面的方法,指导了未来的研究.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 医疗图像的手动注释是耗时的,昂贵的,并引入了偏见.
- 医疗数据的快速增长阻碍了监督学习的进展.
- 自主监督学习 (SSL) 通过利用未标记的数据提供了一个解决方案.
研究的目的:
- 为医疗成像中SSL方法提供全面的审查.
- 从2018年到2024年9月,系统地检查SSL应用程序.
- 引导医疗专业人员将SSL整合到他们的研究中.
主要方法:
- 在医学成像中对SSL进行系统的文献审查.
- 分析各种方法 (CT,MRI,X射线,组织学,超声波) 的研究.
- 应用程序的分类,包括分类,细分和性能增强.
主要成果:
- CT和MRI主导SSL研究,其次是X射线,组织学和超声波.
- 对比式学习比生成式学习更为普遍,除了CT和MRI.
- 分段任务和MRI/超声波分类显示了进一步发展的潜力.
结论:
- SSL有效地解决了医疗成像中手动注释的局限性.
- 该审查突出了研究趋势,并确定了未来SSL探索的领域.
- 对于在医疗保健领域推进人工智能,SSL具有重大前景.
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