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从像素到精密双流深度网络用于病理核细分

Rashid Nasimov¹, Kudratjon Zohirov², Adilbek Dauletov³

¹Department of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-si 13120, Gyeonggi-Do, Republic of Korea.

Bioengineering (Basel, Switzerland)

|August 28, 2025

概括

一个新的深度学习模型,双流超融合网络 (DS-HFN),在细胞病理图像中准确地细分细胞核. 这种计算病理学工具平衡了背景和精度, 改善了疾病诊断和生物标志物分析.

科学领域:

计算病理学
数字病理学
生物医学图像分析

背景情况:

精确的细胞核细分对于计算病理学至关重要,影响疾病诊断和生物标志物分析.
现有的深度学习模型难以平衡全球环境与精确的边界检测, 特别是重叠或变形的核.

研究的目的:

引入一种新的深度学习模型,即双流超融合网络 (DS-HFN),用于强大而准确的细胞核细分.
解决将语义上下文和细微边界细节整合到病理学图像中的挑战.

主要方法:

DS-HFN使用双流编码器同时捕获语义和边缘焦点特征.
一个以注意力驱动的超特征嵌入模块 (HFEM) 融合了这些特征.
双解码器架构和梯度调整损失功能提高了结构精度.

主要成果:

在所有评估指标中,DS-HFN在基准数据集 (TNBC,MoNuSeg) 中表现优于30个最先进的模型.
与现有方法相比,该模型在核细分方面表现出更高的准确性.
DS-HFN也显示了计算成本的降低.

结论:

DS-HFN为数字病理学中精确的细胞核细分提供了强大的解决方案.
该模型精确地划分细胞核对于临床诊断和生物标志物分析至关重要.
DS-HFN通过提高细分精度和效率来推进计算病理学领域.

关键词:

生物医学图像处理病理学中的深度学习双流网络组织病理图像分析核的细分

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