图像与视频对比的对比增强超声波用于计算机辅助诊断
Marina Adriana Mercioni1,2, Cătălin Daniel Căleanu1, Mihai-Eronim-Octavian Ursan1
1Faculty of Electronics, Telecommunications and Information Technologies, Politehnica University Timisoara, Vasile Parvan Street, No. 2, 300223 Timisoara, Romania.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
变压器模型显著改善了使用对比增强超声波 (CEUS) 的焦点肝病变 (FLLs) 的计算机辅助诊断 (CAD). 这些先进的AI系统提高了诊断准确度,为肝脏病理学分析提供了有前途的未来.
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:
- 增强对比的超声波 (CEUS) 对于诊断肝脏焦点病变 (FLL) 至关重要.
- 传统的计算机辅助诊断 (CAD) 系统由于依赖静态图像而难以进行动态的病变特征.
- 整合空间和时间信息是高级CEUS分析的关键.
研究的目的:
- 评估基于变压器的架构在改善肝脏病理的CAD性能方面的有效性.
- 为了比较深度学习模型来分析FLLs的CEUS图像和视频.
- 评估注意力机制在识别微妙的诊断差异方面的潜力.
主要方法:
- 混合变压器神经网络 (HTNN) 结合视觉变压器 (ViT) 和卷积特征被用于基于图像的分类.
- 空间时间卷积神经网络 (CNN),具有长短记忆的CNN (LSTM) 和视频视觉转换器 (ViViT) 被评估为基于视频的任务.
- 绩效的评估是基于分类准确度,并没有手动感兴趣区域 (ROI) 选择.
主要成果:
- 对于FLL分类,HTNN实现了97.77%的准确性,但需要手动的ROI选择.
- 基于视频的模型 (CNN-LSTM,ViViT) 在不需要ROI选择的情况下达到88%的准确性.
- 变压器模型在基于CEUS的肝脏诊断中表现出高准确度.
结论:
- 基于变压器的模型显示了基于CEUS准确的肝脏诊断的巨大潜力.
- 变压器中的注意力机制可以识别微妙的类间差异,减少手动干预的需要.
- 这项研究为更自动和更准确的AI驱动肝病理学分析铺平了道路.
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