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结合式基于多组织的结直肠癌组织学图像的分类方法,使用一种新的混合深度学习框架

Masoud Khazaee Fadafen¹, Khosro Rezaee²

¹Department of Electrical Engineering, Technical and Vocational University (TVU), Tehran, Iran.

Scientific reports

|May 31, 2023

概括

一个新的混合深度学习模型从组织病理学图像准确地分类结直肠癌 (CRC) 组织. 这种方法提高了数字病理学的诊断准确性和计算效率,改善了患者预后评估.

科学领域:

数字病理学数字病理学
计算病理学计算病理学
在瘤学瘤学.

背景情况:

结肠直肠癌 (CRC) 是全球癌症死亡的主要原因.
通过数字病理学进行准确的预后评估至关重要,但由于大型整片图像 (WSIs) 和有限的注释而受到挑战.
现有的深度学习 (DL) 方法可能难以从解剖学组织病理学数据中对CRC组织进行分类.

研究的目的:

开发一种有效的基于深度学习的决策系统,用于结直肠癌组织分类.
解决当前方法论在处理大规模组织病理学图像分析方面的局限性.
使用数字病理学提高CRC预后评估的准确性和效率.

主要方法:

一种混合深度学习模型,将扩展的ResNet (dResNet) 与用于深度特征地图生成的注意模块相结合.
使用邻近组件分析 (NCA) 在特征选择过程中管理计算复杂性.
基于组合学习的深度支持向量机器 (DeepSVM) 的实现,用于最终分类.
在CRC-5000和NCT-CRC-HE-100K数据集上进行验证.

主要成果:

混合模型实现了高精度,在CRC数据集上达到98.75%和99.76%.
与最先进的方法相比,该模型显示出更高的计算效率和速度.
病理学家的标签对于成功分类未见的WSIs至关重要,突出了人工智能和人类专业知识之间的协同作用.

结论:

拟议的混合深度学习方法为结直肠癌中的组织病理图像分析提供了可行和高效的解决方案.
使用这种机制准确的病理图像分类可以导致正确的CRC预测和改善患者管理.
这种方法有可能显著推进癌症预后的数字病理学.

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