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TGIAlign:以文本为导向的双分支双向框架,用于医疗视觉语言中的交叉模式语义对齐
Wenhua Li1, Lifang Wang1, Min Zhao1
1School of Computer Science and Technology, North University of China, Taiyuan, China.
概括
TGIAlign通过在视觉编码的早期使用以病变为中心的线索来增强医疗图像-文本对齐. 这种框架提高了胸部X射线中检索和定位异常的准确性.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 自然语言处理自然语言处理.
背景情况:
- 由于微妙的模式和语义变化,医疗图像-文本对齐是很困难的.
- 目前的方法不够条件视觉特征与诊断语义.
- 这阻碍了细粒度异常检测和跨数据集的稳定对齐.
研究的目的:
- 开发一个新的框架,以改善医疗图像-文本对齐.
- 用文本指导来增强对胸部X射线图像的理解.
- 为了实现稳定和准确的病变定位和检索.
主要方法:
- 提议的TGIAlign,一个以文本为导向的双分支双向对齐框架.
- 使用大型语言模型 (LLM) 来提取病变描述.
- 实现了以文本为导向的图像特征权重 (TGIF) 和双分支双向对齐 (DBBA),用于多尺度的语义调节.
主要成果:
- 在6个数据集中,TGIAlign表现出稳定的top-K检索性能.
- 实现了可靠的文本导向病变定位.
- 展示了早期语义条件和双分支对齐的有效性.
结论:
- 以病变为中心的线索进行早期语义调节,显著改善了医学视觉语言对应.
- TGIAlign为胸部X射线分析和对齐提供了一个强大的解决方案.
- 拟议的框架提高了捕获细粒度异常并保持对齐的能力.
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