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CIMB-MVQA:医疗视觉问题答案的模式特定偏差的因果干预

Bing Liu¹, Lijun Liu², Jiaman Ding¹

¹Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, PR China.

Medical image analysis

|October 31, 2025

概括

本研究介绍了CIMB-MVQA,这是通过解决数据集偏差来改进医疗视觉问答 (Med-VQA) 系统的框架. 它通过干预模式特定偏见来增强模型的稳定性和概括性.

科学领域:

人工智能的人工智能
医疗信息学医疗信息学
计算机视觉计算机视觉

背景情况:

医学视觉问题答案 (Med-VQA) 系统通常依赖于来自数据集偏差和混的虚假线索.
这种依赖阻碍了Med-VQA模型的稳定性和现实世界的概括性.

研究的目的:

提出CIMB-MVQA,一种针对模式特定偏见的因果干预框架.
通过建模和调整混因素来抑制Med-VQA中的交叉模式偏差.

主要方法:

使用前门调整管道 (对比表示学习,特征解,双语义掩盖) 进行不可观察的视觉混.
应用了后门调整策略,使用全球语言偏差词典来观察可见的语言混因素.
集成视觉引导的伪令牌注入和因果图推理以调整因果语义并干预偏差路径.

主要成果:

在多个Med-VQA基准中,CIMB-MVQA显著提高了答案准确性和因果解释性.
在现实的偏差条件下,在不平衡和受控转移数据集上展示了强大的因果概括.
混级别的实验证实了该框架在缓解偏差方面的有效性.

结论:

CIMB-MVQA有效地解决了Med-VQA系统中的模式特定偏差.
该框架增强了模型的稳定性,概括性和因果解释性.
这种方法为开发更可靠的Med-VQA系统提供了一个有希望的方向.

关键词:

因果推理的原因推理.因果干预的干预是因果干预.医学视觉问题解答回答问题多式联运偏差减轻缓解

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