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Image by co-reasoning:一种基于协作推理的隐性数据增强方法,用于双视图CEUS分类

Peng Wan¹, Haiyan Xue², Shukang Zhang¹

¹College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing 211106, China.

Medical image analysis

|April 2, 2025

概括

这项研究引入了一种新的隐性数据增强方法,以改进机器学习,用于双视图对比增强超声波 (CEUS) 分类. 这种方法可以提高乳腺癌和肝癌的诊断准确性,使用有限的临床数据.

科学领域:

医疗成像医学成像
人工智能的人工智能
超声波技术超声波技术超声波技术

背景情况:

有限的双视图对比增强超声波 (CEUS) 数据阻碍了可靠的机器学习模型培训.
由于CEUS数据不足,无法捕捉疾病特异性的纹理变化,影响了模型的概括.
现有的隐性数据增强方法缺乏对视图的语义一致性.

研究的目的:

为双视图CEUS分类提出一种新的隐性数据增强方法.
为了应对数据增强中面试语义一致性的挑战.
使用有限的临床CEUS数据,提高机器学习模型的性能.

主要方法:

开发了一个样本适应性数据增强技术,跨视图进行协作语义推理.
构建了每个超声波视图的特征增强分布,考虑到类内变异.
通过转移可信的语义变化,确保增强视图之间的语义一致性.

主要成果:

验证了对乳腺癌和肝癌双视图CEUS数据集的方法.
乳腺癌的诊断准确度达到89.25%的优越平均值.
对于肝癌,实现了95.57%的优越平均诊断准确度.

结论:

拟议的方法有效地提高模型性能在有限的临床CEUS数据.
在双视图CEUS分析中,隐式数据增强与inter-view语义一致性至关重要.
通过使用CEUS和AI,证明了在癌症检测中提高诊断准确性的潜力.

关键词:

协作增强数据的协作增强.疾病的诊断疾病的诊断双视图对比增强超声波双视图对比增强超声波

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