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基于THMSNet的甲状腺智能诊断

Zhen Rao¹, Tao Yu¹, Xitan Yu²

¹Changde Hospital, Xiangya School of Medicine, Central South University, The First People's Hospital of Changde City, Changde, China.

Frontiers in endocrinology

|December 22, 2025

概括

一个新的深度学习模型THMSNet使用先进的特征提取和注意力机制准确诊断甲状腺结节. 这种人工智能工具提高了诊断的准确性,帮助放射科医生在甲状腺疾病的临床决策.

科学领域:

医疗成像医学成像
人工智能的人工智能
内分泌学在内分泌学.

背景情况:

甲状腺疾病的诊断依赖于区分良性和恶性结节.
像超声波和TI-RADS这样的传统方法有局限性,包括观察者之间的变化和时间限制.
像CNN和变压器这样的深度学习模型看起来很有前途,但在多个规模的特征和全球依赖性方面扎.

研究的目的:

开发一个先进的深度学习模型,用于准确的甲状腺结节诊断.
克服现有的诊断方法和深度学习方法的局限性.
改善甲状腺疾病管理中的临床决策.

主要方法:

提出了THMSNet,一种混合架构,集成了多尺度特征的金字塔结构和长距离依赖的Mamba.
集成的串行通道空间注意模块 (SCSAM) 为增强的特征表示.
利用真实值校准 (TVC) 算法,使预测与病理标准保持一致.
评估了7,288张甲状腺超声波图像 (3,282张良性,4,006张恶性).

主要成果:

THMSNet 实现了 91.15% 的准确性,93.28% 的回忆率和 96.92% 的 AUROC.
性能优于ResNet (86.03%的准确率) 和DenseNet (95.50%的AUROC).
废弃性研究表明,每个模块的性能都显著提高:金字塔架构 (+7.83%准确度),Mamba (+2.99%),SCSAM (+6.94%) 和TVC (+6.94%).

结论:

THMSNet为甲状腺结节诊断提供了一个强大的和临床适用的解决方案.
该模型结合了先进的特征提取,注意力机制和概率校准来实现高精度.
在临床实践中,THMSNet是协助放射科医生的宝贵工具.

关键词:

曼巴建筑曼巴建筑注意力机制注意力机制临床决策支持临床决策支持深度学习是一种深度学习.多尺度的特征提取方法可能性校准的概率.甲状腺结节的诊断甲状腺结节的诊断

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