TransMVAN:多视图聚合网络与用于肺炎诊断的变压器
Xiaohong Wang1, Zhongkang Lu1, Su Huang1
1Institute for Infocomm Research (I²R), A*STAR, 138632, Singapore, Singapore.
Journal of imaging informatics in medicine
|July 8, 2024
概括
这项研究介绍了TransMVAN,这是一种用于在胸部X射线中分类肺炎的新型AI模型. 该方法通过整合多视图功能和双向变压器来提高诊断准确性,在各种数据集上实现高性能.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 通过胸部X射线准确地分类肺炎对于计算机辅助诊断系统至关重要.
- 从放射图像中学习复杂的肺异常结构存在挑战.
研究的目的:
- 提出一个新的多视图聚合网络与变压器 (TransMVAN) 增强肺炎分类在胸部X射线图像.
- 通过结合视和焦点视图来改善肺部异常的特征表示.
- 使用双向多尺度视觉转换器 (biMSVT) 捕捉肺部复杂的区域间关系.
主要方法:
- 开发了TransMVAN,集成多视图学习 (目光和焦点) 以实现丰富的特征表示.
- 引入了biMSVT,以双向地模拟肺部区域之间的长距离依赖和关系.
- 实现了一个封闭的多视图聚合 (GMVA) 来从不同的视图中进行自适应性特征选择.
主要成果:
- 在肺炎分类的两个不同的胸部X射线数据集上达到0.9645和0.9550的曲线下面面积 (AUC).
- 在区分阳性和阴性聚合酶连锁反应 (PCR) 结果时达到0.9761的AUC.
- 达到0.9741的AUC,用于分类正常病例,非COVID-19肺炎和COVID-19肺炎.
结论:
- 拟议的TransMVAN方法在通过胸部X射线图像诊断肺炎方面表现出显著的有效性.
- 多视图信息和先进的变压器架构的集成提高了诊断性能.
- 实验结果验证了TransMVAN在现有比较方法上的优越性.
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