增强知识的视觉语言预培训关于胸部放射图像的胸部放射图像
Xiaoman Zhang1,2, Chaoyi Wu1,2, Ya Zhang1,2
1Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, 200240, Shanghai, China.
知识增强自动诊断 (KAD) 通过使用领域知识进行胸部X射线分析来改进医疗AI. 它显示了专家级,零射击诊断性能,并在少数射击学习中超越现有方法.
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:
- 多模式基础模型在视觉和语言任务中表现有前途,但在专业医疗领域面临局限性.
- 医疗AI需要深入的领域知识,并与细粒度的诊断任务作斗争.
- 现有的模型往往缺乏必要的医学专业知识来准确解释临床数据.
研究的目的:
- 引入知识增强自动诊断 (KAD),这是医疗视觉语言预培训的新方法.
- 利用现有的医学领域知识来提高AI模型在分析胸部X射线和放射学报告中的性能.
- 评估KAD在医疗诊断的零射击和少数射击学习场景中的有效性.
主要方法:
- 开发了KAD,将医疗领域的知识整合到视觉语言预培训中.
- 利用配对的胸部X射线图像和放射学报告进行模型训练.
- 在四个独立的外部X射线数据集上评估KAD.
主要成果:
- KAD实现了与完全监督模型相比的零射击性能.
- 在诊断五种病理中的三种方面,KAD在统计学上显著地优于专家放射科医生.
- 在短暂的设置中,KAD超越了所有以前存在的方法.
结论:
- KAD有效地将领域知识集成到医疗AI中,增强诊断能力.
- 该模型具有很强的临床应用潜力,提供专家级别的性能,数据标签最小或不存在.
- 在将基础模型应用于复杂的医疗任务方面,KAD代表了重大进展.
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