跨模式的子图像检索使用对比的多式模式图像表示
Eva Breznik1,2, Elisabeth Wetzer3,4, Joakim Lindblad1
1Department of Information Technology, Uppsala University, 751 05, Uppsala, Sweden.
Scientific reports
|August 13, 2024
概括
我们开发了一种基于内容的图像检索 (CBIR) 系统,用于跨模式的医学图像搜索. 该系统有效地检索不同成像类型的相似图像,改善癌症诊断和组织特征.
科学领域:
- 医学成像分析分析 医学成像分析
- 计算病理学计算病理学
- 生物医学信息学是生物医学信息学.
背景情况:
- 多式成像对于组织特征和癌症诊断至关重要.
- 计算方面的进步使得在大型医疗数据集中能够发现模式.
- 对于多式联运数据,需要有效和可扩展的图像检索方法.
研究的目的:
- 开发一个独立于应用程序的基于内容的图像检索 (CBIR) 系统,用于跨模式的反向 (子) 图像搜索.
- 为了提高在不同成像模式中检索类似图像的效率和可靠性.
主要方法:
- 结合深度学习用于模态不变表示生成,具有特征提取和词包模型.
- 开发了一个基于内容的跨模式图像检索 (CBIR) 系统.
- 使用亮场和第二波代显微镜图像的数据集来评估系统.
主要成果:
- 拟议的CBIR系统与现有的交叉模式 (子) 图像检索方法相比,显示出更高的性能.
- 该研究强调了等差和不变性质在学习的表示和特征提取器中的重要性.
- 该系统有效地将不同的成像模式嵌入到一个共同的表示空间中.
结论:
- 开发的CBIR系统为医疗应用中的跨模式图像检索提供了高效可靠的解决方案.
- 这些发现支持使用深度学习和强大的特征提取来推进多式联络医疗图像分析.
- 这种方法有可能通过改进的图像搜索功能来增强癌症诊断和组织特征.
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