视觉语言诊断语义增强用于医疗报告生成
Jiahong Chen1, Guoheng Huang1, Xiaochen Yuan2
1School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China.
Journal of biomedical informatics
|January 2, 2025
概括
本研究引入了一种新的视觉语言诊断语义增强模型 (VLDSE) 用于医学报告生成. VLDSE模型通过增强视觉语义和语义一致性来提高生成报告的准确性和临床相关性.
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:
- 生成模型有助于从图像中生成医疗报告,但难以准确检测病变和精确的临床术语.
- 现有的方法往往无法捕捉微妙的病变细节或使用不一致的诊断语言.
研究的目的:
- 开发一种先进的模型,用于生成高质量的医疗报告,使用精确的临床术语准确地描述病变区域.
- 为了弥合医学图像中的视觉特征和报告中的语言描述之间的语义差距.
主要方法:
- 提出了一个视觉语言诊断语义增强 (VLDSE) 模型.
- 在图像和报告语义一致性 (IRSC) 模块中使用监督对比学习.
- 引入了视觉语义合格和量化 (VSQQ) 和后期语义校正 (PSC) 模块,用于增强语义.
主要成果:
- VLDSE模型在IU X-RAY和MIMIC-MV数据集上显示了显著的改进.
- 在IU X-RAY上获得18.6%的BLEU-4得分,比基线改善12.7%.
- 在MIMIC-MV数据集上,BLEU-1得分提高了10.7%.
结论:
- VLDSE模型有效地生成准确和流的医疗报告,特别是在描述病变区域时.
- 拟议的模块增强了模型解释视觉信息和使用精确临床语言的能力.
- 这种方法有望提高医疗成像诊断的准确性和效率.
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