视觉语言病理学基础模型的知识增强预培训,用于癌症诊断
Xiao Zhou1, Luoyi Sun2, Dexuan He3
1Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China.
Cancer cell
|February 20, 2026
概括
我们开发了KnowledgE增强病理学 (KEEP),这是一个新的基础模型,将医学知识整合在一起,以改善癌症诊断. 通过结合疾病知识,KEEP 增强了视觉语言模型,显著提高了性能,特别是在罕见的癌症亚型中.
科学领域:
- 计算病理学计算病理学
- 医学中的人工智能.
- 医疗图像分析 医疗图像分析
背景情况:
- 视觉语言基础模型显示了计算病理学的潜力.
- 目前的模型主要是数据驱动的,缺乏明确的医学知识整合.
研究的目的:
- 引入KnowledgE增强病理学 (KEEP),这是一个基础模型,系统地将疾病知识纳入癌症诊断预训练中.
- 改善对疾病关系和病理学图像中的形态模式的理解.
主要方法:
- 利用了一个全面的疾病知识图表 (11,454种疾病,139,143种属性).
- 重组了数以百万计的病理学图像-文本对,并将其分为与疾病本体学层次结构一致的语义结构组.
- 采用知识增强的预训练,在层次的语义空间内调整视觉和文本表示.
主要成果:
- 在18个公共基准 (14,000多张全幻灯片图像) 和4个机构罕见癌症数据集 (926例) 中,KEEP的表现始终优于现有的基础模型.
- 显著提高了性能,特别是在罕见的癌症亚型中.
- 建立了知识增强的预训练,作为对等级语义空间中的表示进行对齐的有效方法.
结论:
- 知识增强视觉语言建模是推进计算病理学的强大范式.
- KEEP提供了一种新的方法来整合医学知识,提高诊断准确性和理解复杂疾病.
- 这些发现表明,在病理学中开发更强大,更易于解释的AI工具是一个有希望的方向.
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