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通过光学时间延伸成像流细胞计与多实例学习进行结肠直肠癌的无标签类型化

Sini Pi^1,2, Liye Mei^2,3, Liang Tao⁴

¹School of Medicine, Wuhan City College, Wuhan, China.

Journal of biophotonics

|April 8, 2025

概括

这项研究引入了一种新的无标签方法,用于结直肠癌 (CRC) 类型,使用光学时间延伸成像流动细胞计和AI. 这种方法可以准确地区分癌细胞,有助于更快的诊断.

科学领域:

生物医学工程生物医学工程
计算病理学计算病理学
癌症研究癌症研究

背景情况:

大肠直肠癌 (CRC) 是一种主要的胃肠道恶性瘤.
目前的病理分析是劳动密集型的,限制了广泛的应用.
了解瘤微环境对于CRC研究至关重要.

研究的目的:

开发一种用于结直肠癌类型的无标签方法.
为了利用光学时间延伸 (OTS) 成像流细胞计和多实例学习进行CRC分析.
提高癌症诊断的效率和可访问性.

主要方法:

开发了一个集成OTS成像和微流体细胞聚焦的高通量细胞图像采集系统.
来自10个临床样本的363,931个细胞图像被捕获.
采用具有多层次注意力机制的多实例学习框架来处理细胞多样性和异质性.

主要成果:

提出的方法在区分正常细胞和癌细胞方面取得了85.78%的准确性.
该系统在所有10个临床样本中展示了有效的CRC类型表现.
为了高效的无标签CRC打字,采用了多数投票机制.

结论:

使用OTS成像和多实例学习开发的无标签CRC打字方法显示了显著的潜力.
这种方法为传统的病理学分析提供了更有效和更可扩展的替代方案.
进一步验证可能会导致改善结直肠癌的诊断工具.

关键词:

结肠直肠癌是什么意思多级别的学习多级别的学习.多层次的注意力.光学时间延伸成像技术投票机制投票机制

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