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甲状腺结节的ResNet-ViT分类模型使用超声波图像和临床信息

Le Zhou¹, Cuicui Zhao², Lijuan Zhu¹

¹Department of Mathematics and Physics, Shijiazhuang Tiedao University, Shijiazhuang city, China, Shijiazhuang, 050043, CHINA.

Biomedical physics & engineering express

|January 28, 2026

概括

一个新的ResNet-ViT多式模式模型使用图像和临床数据准确区分良性和恶性甲状腺结节,优于现有方法,提高诊断准确度.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

甲状腺结节很常见,通过超声波区分良性和恶性类型会带来误诊的风险.
计算机辅助诊断 (CAD) 对于提高诊断准确性和患者的治疗结果至关重要.

研究的目的:

开发和评估一种新的深度学习模型ResNet-ViT,用于更好地预测甲状腺结节恶性瘤.
评估结合图像和临床数据的多式联络方法的有效性.

主要方法:

设计了一个混合ResNet-ViT模型,集成ResNet18用于本地功能和Vision Transformer (ViT) -l-16用于全球功能.
通过使用MCB算法将其图像特征与临床数据融合起来,进一步增强了ResNet-ViT模型,创建了一个多式模式模型.
在DDTI和TN5000数据集上使用5倍交叉验证评估性能,并与其他八种已建立的模型进行比较.

主要成果:

与其他八种方法相比,ResNet-ViT模型显示出优异的预测性能.
ResNet-ViT多式联络模型实现了显著改善的诊断准确性,超过了所有其他模型.
将周围组织信息纳入数据集,提高了模型区分良性和恶性结节的能力.

结论:

ResNet-ViT多式模式有效地整合了成像和临床特征,为甲状腺结节分类提供了稳定而准确的方法.
这种先进的CAD方法具有显著的潜力,可以改善甲状腺结节的诊断,减少误诊率,并有利于患者的护理.

关键词:

这就是ResNet ResNet.这里是ViT ViT ViT 功能融合功能融合功能多式联络模式的模型甲状腺结节甲状腺结节

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