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多重聚合组合样本重量复合网络用于病理图像细分

Dawei Fan¹, Zhuo Chen¹, Yifan Gao¹

¹College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.

Artificial intelligence in medicine

|August 26, 2025

概括

此摘要是机器生成的。

CDNet是一个用于数字病理学的核心细分的新型网络, 解决了模糊的边界和领域转移等挑战. 这种方法显著提高了数据集的细分精度和概括性.

关键词:

因果推断数字病理学功能融合核的细分虚假的相关性

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科学领域:

数字病理学
医学图像分析
计算机视觉

背景情况:

细胞核细分对于病理图像分析至关重要,影响诊断和研究.
现有方法面临挑战,包括模糊的边界,域位移和核分布不均.
这些障碍阻碍了准确可靠的病理图像细分.

研究的目的:

提出一个创新的网络,CDNet,用于数字病理学中强大的核心细分.
解决当前细分技术的局限性,提高准确性和概括性.
改善病理图像分析,以获得更好的诊断和研究结果.

主要方法:

引入了CDNet,其中包括多元化聚合转换 (DAC) 以确保边界清晰.
整合了一个因果推理模块 (CIM) 以提高跨领域的概括性.
开发了一个稳定加权组合损失函数来解决核分布不均.

主要成果:

在MoNuSeg,GLySAC和MoNuSAC数据集上,CDNet表现出卓越的表现.
在欧盟的平均交叉点 (mIoU) 和子相似系数 (DSC) 中取得了显著的改善.
在多种病理图像数据集中展示了强大的概括能力.

结论:

CDNet有效地克服了数字病理的核心细分方面的关键挑战.
拟议的DAC,CIM和损失函数有助于提高细分的准确性和稳定性.
CDNet为病理图像分析和临床应用提供了有前途的进展.