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HF-VLP:一种多式视觉语言预训练模型,用于诊断心力衰竭
Huiting Ma1, Dengao Li1, Guiji Zhao2
1College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China; Key Laboratory of Data Governance and Intelligent Decision Making of Shanxi Province, Taiyuan, 030024, China; Intelligent Perception Engineering Technology Center of Shanxi, Taiyuan, 030024, China.
概括
一个新的多式模式模型,HF-VLP,通过整合胸部X射线和报告,有助于早期发现心力衰竭 (HF). 它解决了杂的标签,并使用参数有效的微调来提高诊断准确性.
科学领域:
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
- 医疗保健的自然语言处理.
背景情况:
- 心力衰竭 (HF) 发病率的上升需要先进的诊断工具.
- 多模式视觉语言预训练显示出医疗诊断的前景.
- 挑战包括医疗数据集中的噪音标签和需要有效的模型适应.
研究的目的:
- 开发一种多式视觉语言预训练模型 (HF-VLP),以改进心力衰竭检测.
- 为应对噪音标签的挑战,并提高模型效率,用于临床应用.
- 合并胸部X射线和放射学报告数据,以进行全面的患者评估.
主要方法:
- 开发了HF-VLP,一种多式视觉语言预训练的心力衰竭模型.
- 在预训练期间实施标签校准损失以减轻噪音标签.
- 使用分解单数值重量分解低级调整 (PEFT) 进行高效的微调 (<1%参数).
- 集成的胸部X射线和放射学报告功能通过动态融合图模块.
主要成果:
- 在Open-I数据集上达到83.67%的平均AUC,在PPL-CXR数据集上达到91.28%的多症状预测.
- 与零射击相比,参数有效微调方法提高了诊断率.
- 该模型证明了患者症状的准确分类,有助于临床诊断.
结论:
- HF-VLP有效地整合了多式联络医疗数据,用于诊断心力衰竭.
- 开发的方法解决了医疗预培训的关键挑战,包括噪音标签和高效的适应.
- 这种模型显示出有很大的潜力,可以帮助临床医生在心力衰竭的早期和准确诊断.
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