一个新的混合卷积和变压器网络用于淋巴瘤分类
Mohamed Yacin Sikkandar1, Sankar Ganesh Sundaram2, Muteb Nasser Almeshari3
1Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia. m.sikkandar@mu.edu.sa.
Scientific reports
|July 19, 2025
概括
一个新的AI模型,淋巴瘤分类混合卷积和变压器网络 (HCTN-LC),从整个幻灯片图像 (WSIs) 中准确地分类淋巴瘤亚型. 这种先进的深度学习方法提高了诊断精度,以获得更好的患者结果.
科学领域:
- 病理学 病理学 病理学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 从整个幻灯片图像 (WSIs) 进行淋巴瘤分类是具有挑战性的,原因是亚型的形态相似性.
- 传统方法缺乏客观性和一致性,需要先进的AI解决方案.
- 目前的模型难以整合本地和全球特征以进行准确的诊断.
研究的目的:
- 开发和评估混合深度学习框架,用于精确的淋巴瘤亚型分类.
- 为了提高AI模型在血液病理学中的解释性.
- 解决现有模型在捕获本地和全球图像特征方面的局限性.
主要方法:
- 提出了用于淋巴瘤分类 (HCTN-LC) 的混合卷积和变压器网络.
- 采用双通道架构,将SqueezeNet用于本地功能和Vision Transformer (ViT) 用于全球环境.
- 引入了一个功能融合和增强模块 (FFEM),用于动态功能集成.
主要成果:
- 在CLL,FL和MCL亚型的WSI数据集上,HCTN-LC实现了高性能.
- 实现了总体准确率为99.87%,灵敏度为99.87%,特异性为99.93%,AUC为0.9991.
- Grad-CAM可视化证实了该模型的重点是诊断相关的区域,证明了可解释性.
结论:
- HCTN-LC框架为淋巴瘤亚型分类提供了卓越的性能和可解释性.
- 证明了在资源较少的环境中临床部署的潜力.
- 为血液病理学诊断提供强大的AI工具,改善患者护理.
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