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生物医学视觉指令调整与临床医生偏好调整

Hejie Cui1,2, Lingjun Mao3, Xin Liang3

  • 1Stanford University.

Advances in neural information processing systems
|February 28, 2025
PubMed
概括

本研究介绍了BioMed-VITAL,这是一个使用临床医生的偏好创建专门数据集以调整生物医学多式模式的框架. 这提高了医疗视觉问答和开放视觉聊天应用程序的性能.

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科学领域:

  • 人工智能的人工智能
  • 生物医学信息学 生物医学信息学
  • 机器学习 机器学习

背景情况:

  • 多模式基础模型在视觉和文本理解方面表现有前途.
  • 将这些模型适应于生物医学等专业领域,需要大量的特定领域指令数据集.
  • 现有的自动数据集策划方法缺乏与领域专业知识的明确对齐.

研究的目的:

  • 提出一个以数据为中心的框架,BioMed-VITAL,用于调整生物医学多式模式基础模型.
  • 在生成和选择指令数据时,要纳入临床医生的偏好.
  • 提高模型在专门的生物医学应用中的性能.

主要方法:

  • 开发了BioMed-VITAL,一个框架,将临床医生的偏好整合到数据生成和选择中.
  • 利用GPT-4V与临床医生选择的演示来生成与偏好一致的候选数据.
  • 训练了一种选择模型,以提炼临床医生和政策指导的偏好,以获得高质量的数据选择.
  • 调整生物医学多式联络基础模型,使用精心策划的指令后数据.

主要成果:

  • 与BioMed-VITAL数据调整的模型显示,在视觉聊天中出现了显著的改善 (18.5%的相对增加).
  • 在医学视觉问题答案 (VQA) 中获得高达81.73%的高胜率.
  • 证明了临床医生偏好调整在医疗指令调整中的有效性.

结论:

  • 通过结合临床医生的偏好,BioMed-VITAL有效地增强了生物医学多式联络基础模型.
  • 提出的以数据为中心的方法导致在专门的医疗人工智能任务中表现出色.
  • 开发的数据集和模型是公开可用的,以推进生物医学AI研究.