在整个幻灯片成像中探索多实例学习:当前和未来的观点
Jikai Yu1, Hongda Chen1, Lianxin Hu1
1School of Information Engineering, Huzhou University, Huzhou, ZheJiang 313000, China.
Pathology, research and practice
|May 14, 2025
概括
整个幻灯片图像 (WSIs) 对病理学至关重要,但对GPU来说太大了. 多实例学习 (MIL) 能够有效地分析这些大型 WSIs 以实现自动诊断.
科学领域:
- 数字病理学数字病理学
- 计算病理学计算病理学
- 机器学习在医学中的应用
背景情况:
- 整个幻灯片图像 (WSIs) 对于诊断病理学至关重要,但由于它们的大小,会带来计算挑战.
- 在GPU上直接处理千兆字节规模的WSIs很困难,这阻碍了自动选和诊断算法开发.
研究的目的:
- 系统地审查研究进展和多实例学习 (MIL) 的应用,以分析世界互联网.
- 概述MIL在WSI处理和分析癌症检测和亚型分类的主流技术方面的优势和改进.
主要方法:
- 系统的文献综述来自Web of Science,IEEE Xplore和PubMed的90多篇文章.
- 应用到WSI分析的主流MIL技术的核心特征和性能分析.
- 专注于增强WSI处理MIL能力的方法.
主要成果:
- 多实例学习 (MIL) 为WSI分析提供了一个有效的计算框架.
- 诸如数据预处理,多尺度特征融合,实例选择和变压器模型等技术显著提高了WSIs上的MIL性能.
- 在诸如癌症检测和WSIs的亚型分类等任务中,MIL显示出前景.
结论:
- MIL是一种强大的方法,可以克服WSI分析中的计算限制.
- MIL方法的进步对于开发自动化病理诊断工具至关重要.
- 未来的研究应该专注于进一步增强MIL技术,以便在病理学中更广泛地采用.
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