在使用标签耐噪声模型的对比增强超声波中对体肝脏创伤进行强大的自动分级.
Tianci Zhang1,2, Rui Li1,2, Zhaoming Zhong3
1School of Biological Science and Medical Engineering, Southeast University, Nanjing, 211189, China.
Journal of imaging informatics in medicine
|March 10, 2025
概括
这项研究引入了一种新型的人工智能模型,通过对比增强超声波 (CEUS) 图像准确诊断肝损伤. 标签耐噪声CNN转换器混合架构 (LNRHA) 有效地处理图像噪声和诊断变异,以改进分类.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 创伤外科 手术 创伤外科
背景情况:
- 与对比度增强的超声波 (CEUS) 显示出诊断肝损伤的前景,肝损伤是的腹部创伤死亡率的主要原因.
- 对肝损伤的CEUS图像分析是具有挑战性的,因为斑点噪声和复杂的视觉特征,严重依赖放射科医生的专业知识.
- 观察者内部和观察者间的变异性显著影响基于CEUS的肝损伤诊断的准确性.
研究的目的:
- 开发一个自动化系统,使用CEUS图像准确地分类肝损伤.
- 为了应对图像噪声和诊断变异性在CEUS肝损伤评估中的挑战.
- 通过先进的AI技术,提高肝损伤诊断的可靠性和效率.
主要方法:
- 提出了一个基于CNN转换器的自我上下文双变压器 (SCDT) 模块,用于从CEUS图像中提取特征.
- 引入了基于信任的标签过器 (CLF) 模块,以识别和减轻由观察者变化引起的注释噪声.
- 开发了一个新的损失函数来惩罚不确定的数据,使模型能够利用所有数据,同时防止过度拟合.
主要成果:
- 拟议的标签-抗噪声-CNN-变压器混合架构 (LNRHA) 在内部肝损伤CEUS数据集上实现了有希望的性能.
- 与最先进的方法相比,LNRHA表现优越,特别是在带有标签噪声的数据集上.
- 该模型有效地处理CEUS肝脏创伤成像中固有的斑点噪声和诊断变异.
结论:
- 该LNRHA提供了一个有效和强大的解决方案,用于自动化肝损伤分类从CEUS图像.
- 开发的方法通过解决标签噪声和观察者变异性,显著提高了诊断准确性.
- 这种由人工智能驱动的方法有可能提高CEUS在治疗腹外伤的临床效用.
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