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基于记忆的交叉模态语义对齐网络用于放射学报告生成

Yitian Tao, Liyan Ma, Jing Yu

IEEE journal of biomedical and health informatics

|April 24, 2024

概括

这项研究引入了一种基于记忆的新型模型,用于从图像中生成放射学报告. 该方法通过调整跨模态语义和利用临床记忆来提高准确性,优于现有的方法.

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:

自动化放射学报告生成有助于放射科医生和疾病诊断.
目前的方法很难从图像和报告中获取关键的疾病信息.
这种限制阻碍了生成流和准确的报告.

研究的目的:

开发一个先进的模型,用于准确和流的放射学报告生成.
为了应对学习医学图像和报告之间的潜在关系的挑战.
提高自动诊断工具的临床实用性.

主要方法:

提出了一个基于内存的交叉模式语义对齐模型 (MCSAM),使用编码器-解码器架构.
整合了一个长期临床记忆库,用于与疾病相关的表示和先前知识检索.
引入了一个语义对齐模块 (SAM) 以实现跨模式一致性和视觉特征嵌入生成.
利用可学习的内存令牌作为提示来增强解码器内的报告生成.

主要成果:

在生成放射学报告方面,MCSAM表现出卓越的性能.
该模型成功地学习了放射图像和相应的报告之间的潜在关系.
在MIMIC-CXR数据集上取得了最先进的结果.

结论:

拟议的MCSAM有效地增强了自动化放射学报告的生成.
基于记忆的学习和交叉模式的语义对齐是提高报告准确性和流性的关键.
这种方法为减少放射科医生的工作量和帮助疾病诊断提供了一个有希望的解决方案.

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