甲状腺结节与多实例卷积神经网络的风险评估
Da Yu1, Tingting Song2, Yancheng Yu1
1Department of Ultrasound, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China.
Frontiers in oncology
|August 8, 2025
概括
使用多实例学习 (MIL) 和卷积神经网络 (CNN) 的新型人工智能 (AI) 模型可以从超声波图像中改善甲状腺癌的诊断. 这种人工智能工具有助于风险分层,可能减少不必要的程序.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 在瘤学瘤学.
背景情况:
- 超声波是甲状腺结节的主要成像方法.
- 目前用于甲状腺癌诊断的AI模型的性能有限.
- 需要改进甲状腺结节的AI辅助诊断工具.
研究的目的:
- 提出一种新的多实例学习 (MIL) 卷积神经网络 (CNN) 模型,用于基于超声波的甲状腺癌诊断.
- 使用人工智能提高甲状腺结节的诊断性能和风险评估.
- 评估开发的AI框架的临床实用性.
主要方法:
- 开发了一种MIL-CNN模型,从实例级图像中提取结节级超声波特征.
- 利用注意力机制来聚合特征和定位关键诊断指标.
- 在甲状腺结节患者2000张超声波图像的数据集上训练和测试模型.
主要成果:
- 与其他分类模型相比,拟议的MIL-CNN模型表现出优越的性能.
- 关键性能指标,包括准确性,精度,回忆,F1-Score和AUC显示显著改善.
- 统计分析证实了显著的性能差异 (p <0.0001).
结论:
- 该MIL-CNN框架是有效的非侵入性甲状腺结节风险分层.
- 人工智能模型支持明智的临床决策,可能减少不必要的活检和手术.
- 这项研究强调了先进AI在甲状腺癌诊断中的临床实用性.
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