走向自动化报告:一个支气管镜报告数据集用于增强多模式性 大型语言模型
Xingjian Luo1,2, Xinyan Huang3, Xusheng Liang1
1Centre for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong SAR, China.
Scientific data
|February 3, 2026
概括
一个新的数据集,BERD,增强了用于呼吸系统疾病诊断的多模式大语言模型 (MLLMs). 在BERD上微调MLLM可以提高人工智能生成的支气管镜报告的准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 呼吸系统医学 呼吸系统医学
背景情况:
- 支气管镜检查对于诊断和管理呼吸系统疾病至关重要.
- 多模式大型语言模型 (MLLMs) 在医疗报告生成方面表现有前途.
- 现有的数据集对于复杂的支气管镜病例缺乏全面的注释,限制了MLLM培训.
研究的目的:
- 介绍支气管镜检查报告数据集 (BERD).
- 提供一个数据集,详细注释图像报告关系在支气管镜检查.
- 提高MLLM的性能,生成准确和全面的支气管镜检查报告.
主要方法:
- 开发了BERD,一个包含3692份支气管镜检查报告的数据集.
- 由专家临床医生注释了6,330张图像,附有详细的文本描述和分类标签.
- 在BERD数据集上微调的最先进的MLLM.
主要成果:
- 该BERD数据集包括多功能和详细的发现描述.
- 在BERD上微调MLLM显著提高了其报告生成准确性.
- 改进的MLLM性能证明了数据集在支气管镜检查中对AI的有效性.
结论:
- 欧洲风险投资委员会 (BERD) 解决了支气管镜数据集中需要全面注释的需求.
- 该数据集为MLLMs更好地学习图像报告关系.
- 欧洲开发银行通过改进报告生成,在呼吸系统疾病诊断中推进人工智能应用.
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