一个视觉关注驱动的语言框架,用于医疗报告的生成
Merve Varol Arısoy1, Ayhan Arısoy2, İlhan Uysal3
1Bucak Faculty of Computer and Informatics, Information Systems Engineering Department, Burdur Mehmet Akif Ersoy University, Burdur, Turkey. mvarisoy@mehmetakif.edu.tr.
Scientific reports
|March 29, 2025
概括
本研究介绍了医疗视觉注意力生成 (MedVAG) 模型用于自动化医疗报告生成. 通过将先进的视觉和语言模型与新的注意力机制相结合,MedVAG提高了诊断准确度,并支持放射科医生.
科学领域:
- 人工智能的人工智能
- 医学成像分析 医学成像分析
- 自然语言处理自然语言处理.
背景情况:
- 自动化医疗报告生成对于高效的临床工作流程至关重要.
- 现有的方法经常在报告生成中与语义连贯性和诊断准确性作斗争.
研究的目的:
- 引入医疗视觉注意力生成 (MedVAG) 模型用于自动化医疗报告生成.
- 提高人工智能生成的医疗报告中的语义连贯性和诊断准确性.
主要方法:
- MedVAG模型集成视觉转换器 (ViT) 进行视觉特征提取和GPT-2用于语言建模.
- 采用基于图的特征融合和多重注意力机制 (共同注意,交叉注意,记忆引导的注意).
- 该模型在IU X-Ray和COV-CTR数据集上进行了评估.
主要成果:
- 在自然语言生成指标 (BLEU,METEOR,ROUGE,CIDEr) 上,MedVAG取得了最先进的表现.
- 临床有效性测量表明该模型的诊断准确性.
- 废弃研究证实了注意力机制和特征融合的重要性.
结论:
- 作为放射科医生的辅助技术,MedVAG具有显著的潜力.
- 该模型可以帮助减少放射科医生的工作量,并提高诊断准确度.
- 先进的注意力和融合技术是医疗报告生成视觉和文本信息对齐的关键.
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