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弥合多层次差距:双向互惠循环框架,用于以文本为导向的标签高效细分在心声回声学中.
Zhenxuan Zhang1, Heye Zhang2, Tieyong Zeng3
1School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China; Bioengineering Department and Imperial-X, Imperial College London, W12 7SL London, UK.
Medical image analysis
|March 12, 2025
概括
在心声回声学中,以文本为导向的视觉理解得到了双向反向循环 (BRC) 框架的改进. 这种方法弥合了图像和文本之间的差距,提高了细分的准确性,减少了标记数据的需求.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 文本引导的视觉理解为心声回声学提供了一个有希望的方法,通过将临床信息嵌入到视觉任务中,减少了对广泛标记数据集的需求.
- 对比性语言-图像预训练 (CLIP) 方法适应使用图像-文本特征的预训练网络,但在医学成像中面临多层次 (空间,上下文,域) 差距的挑战.
研究的目的:
- 解决心声回声学中的多层次图像-文本差距,以改善下游任务学习.
- 为密集的预测任务开发一个新的框架,有效地弥合这些差距.
主要方法:
- 提出了一种双向互惠循环 (BRC) 框架,通过金字塔互惠对齐来对齐全球和本地图像-文本特征.
- 实施了前向和反向映射 (文本特征,特征文本/图像) 的一致性执行,以捕捉上下文关系.
- 集成了一种跨模式的注意力机制,以适应特定的细分任务,并通过复杂的文本信息引导它们.
主要成果:
- 在细分任务上实现了最先进的性能,子相似系数 (DSC) 为95.2%,超过了22种现有方法.
- 显示了显著的准确性改进和减少对标记数据的依赖,DSC从81.5%增加到86.6%,仅使用1%的标记数据与文本辅助.
- 验证了对11,048名患者的广泛实验.
结论:
- BRC框架有效地弥合了心声回声学中的多层次图像-文本差距.
- BRC显著提高了医疗图像分析中的细分精度和数据效率.
- 这种方法有可能促进临床任务学习,减少标签要求.
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