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基于特定领域的转移学习和多模型特征融合的结直肠癌的组织病理学分类

Qi Ke^1,2,3, Yan Chai Hum⁴, Wun-She Yap³

¹School of Big Data and Artificial Intelligence, Guangxi University of Finance and Economics, Nanning, China.

Scientific reports

|October 8, 2025

概括

这项研究引入了一个AI深度学习框架,用于从组织病理学图像准确诊断结直肠癌 (CRC). 该模型显著提高了分类准确性,帮助病理学家并减少了诊断工作量.

科学领域:

计算病理学计算病理学
人工智能在瘤学中的应用
医疗图像分析医学图像分析

背景情况:

结肠直肠癌 (CRC) 诊断严重依赖于组织病理学图像分析,这是耗时且容易出现错误的.
复杂的CRC组织病理图像,包括多尺度结构和阶级不平衡,对自动化分析提出了挑战.

研究的目的:

开发一个自动化的深度学习框架,以提高结直肠癌诊断的准确性和效率.
解决CRC图像分类方面的挑战,包括多尺度特征,噪音标签,类失衡和细粒度亚型识别.

主要方法:

使用特定域的转移学习,从基因病理图像中提取相关特征.
一个多头自我注意力机制融合了来自多个预训练模型的功能.
一个多层感知子 (MLP) 分类器被用于最终的CRC分类.

主要成果:

该框架实现了高分类准确率:EBHI (200×子集) 的99.68%,Chaoyang的86.72%和COAD数据集的99.44%.
在多样化和复杂的基因病理图像条件中表现出强大的概括能力.
在公开可用的CRC数据集上验证的有效性.

结论:

域特定转移学习,多模型特征融合和注意力机制的结合对CRC分类有效.
拟议的模型提供了一个可靠和有效的工具来支持病理学家,潜在地减少手工工作量和提高诊断一致性.

关键词:

结肠直肠癌是一种癌症.特定领域的模型.功能融合的特点是:组织病理学分类转移学习转移学习

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