使用基于深度学习特征的超声波图像来诊断甲状腺结节:在线动态名图和梯度加权类激活映射
Wenwu Lu1, Di Zhang1, Wang Zhou1
1Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Quantitative imaging in medicine and surgery
|July 3, 2025
概括
结合超声波图像和深度学习 (DL) 的新综合模型显著改善了甲状腺结节 (TNs) 的诊断,提高了放射科医生在区分良性病例和恶性病例方面的准确性.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 放射学 放射学是一门学科.
背景情况:
- 甲状腺结节 (TN) 是常见的,需要精确区分良性和恶性类型,以防止过度治疗.
- 目前对TNs的诊断方法需要改进,以提高准确性和效率.
研究的目的:
- 开发和可视化一个综合模型来评估跨国网络.
- 提高放射科医生的诊断能力,特别是年轻的放射科医生,在评估TN时.
- 为了改善良性和恶性甲状腺结节之间的差异化.
主要方法:
- 追溯分析1501张超声波 (US) 图像的TN,分为培训和验证集.
- 微调五个ImageNet预训练的深度学习 (DL) 模型,使用转移学习对美国图像进行微调.
- 综合美国图像特征和DL特征的综合模型的开发,使用梯度加权类激活映射 (Grad-CAM) 进行可视化.
主要成果:
- 与美国模型 (0.787) 相比,DL模型在测试组上实现了0.875的接收器操作特征 (ROC) 曲线 (AUC) 下的优越面积.
- 综合综合模型显示显著改善,测试组AUC为0.907.
- 净重新分类指数 (NRI) 证实DL模型输出 (热图和分数) 提高了放射科医生的分类准确性.
结论:
- 综合模型结合了美国图像和DL特征,在区分良性和恶性TN方面表现出强大的诊断性能.
- 这种方法提供了一个有前途的工具,以帮助放射科医生准确诊断甲状腺结节.
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