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通过可解释的深度学习和Jensen-Shannon可靠性指数对HEp-2标本进行自动分类

A Mencattini¹, T Tocci², M Nuccetelli³

¹Department of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133 Rome, Italy; Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, 00133 Rome, Italy.

Artificial intelligence in medicine

|December 5, 2024

概括

这项研究介绍了一种先进的AI平台,用于分析反核抗体 (ANA) 测试图像. 该系统准确地分类HEp-2细胞图像和ANA模式,提高结缔组织疾病的诊断准确度.

科学领域:

免疫学免疫学免疫学
计算生物学计算生物学
医学诊断医学诊断医学诊断

背景情况:

通过间接免疫光 (IIF) 使用人类上皮类型2 (HEp-2) 细胞的抗核抗体 (ANA) 测试是诊断连接组织疾病的黄金标准.
对HEp-2图像的计算机辅助分析对于推进ANA模式分类至关重要,利用机器学习.

研究的目的:

开发用于自动化HEp-2图像分析和ANA图案分类的创新平台.
提高计算机辅助ANA测试的准确性,稳定性和可解释性.

主要方法:

利用转移学习与预训练的深度学习模型用于HEp-2图像分析.
实现了无监督的深度描述,对不平衡的数据集进行了新的特征选择,并对多医院数据进行了独立验证.
引入了经过修改的梯度加权类激活映射以实现区域可解释性和基于Jensen-Shannon分歧的样本质量指数.

主要成果:

在强度和ANA模式识别方面实现了异常高的性能,超过了最先进的方法.
通过消除细胞细分的需要,通过依赖统计样本分析来证明强度和多功能性.
在不同的医院数据集中验证了该方法的跨硬件兼容性和可靠性.

结论:

开发的平台为ANA测试中的自动化HEp-2图像分析提供了强大,多功能和高度准确的解决方案.
由人工智能驱动的方法显著改善了ANA模式的分类,有助于诊断连接组织疾病.
未来的工作将扩展这种方法来识别复杂的模式,包括线粒状识别和混合模式.

关键词:

深度学习是一种深度学习.可以解释Grad-CAM的可解释性.HEp-2 计算机辅助分析詹森 - 香农分歧指数转移学习转移学习

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