在COVID-19评估中,用于胸部X射线图像检索的三重受限深度哈希
Linmin Wang1, Qianqian Wang2, Xiaochuan Wang1
1School of Mathematics Science, Liaocheng University, Liaocheng, Shandong, 252000, China.
概括
一个新的三重受约束的深度哈希框架改善了用于COVID-19分析的胸部X射线检索. 这种方法增强了功能学习,以实现更准确的医学图像搜索和自动诊断.
科学领域:
- 医学成像医学成像
- 人工智能的人工智能
- 计算机辅助诊断是一种计算机辅助的诊断.
背景情况:
- 胸部放射图像对于COVID-19分析至关重要.
- 深度哈希方法对于放射学图像搜索是有效的,但往往错过了复杂的图像关系.
- 现有的方法难以捕捉医疗图像之间的细微相似之处和差异.
研究的目的:
- 开发一种新的三重受约束深度哈希 (TCDH) 框架,用于增强胸部放射学图像检索.
- 通过更好的医学图像搜索来提高自动化COVID-19分析的准确性和效率.
- 解决现有的深度哈希方法在捕获复杂的图像关联方面的局限性.
主要方法:
- 开发了一个两阶段的TCDH框架:特征提取和图像检索.
- 引入了三重约束来分组相似的图像和分离不相似的图像.
- 整合了一个辅助图像重建任务,以保存解剖图像结构.
- 将提取的特征转换为二进制哈希代码,以实现高效的数据库搜索.
主要成果:
- 与最先进的方法相比,TCDH框架在自动图像搜索中表现出优异的性能.
- 对30386张胸部X射线图像进行了广泛的实验,验证了该方法的有效性.
- 学习的哈希代码准确地捕获语义信息,以便准确地检索.
结论:
- 提议的TCDH框架显著提高了用于COVID-19分析的胸部放射图像检索.
- 三重约束和图像重建的结合提高了特征的可辨别性.
- 这种方法为自动化医学图像搜索和诊断支持提供了一个有希望的解决方案.
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