使用病理学基础模型的多实例学习有效预测病诊断和临床分类
medRxiv : the preprint server for health sciences
|June 30, 2025
概括
病理学基础模型与多重实例学习 (MIL) 结合,在病理学中显示出强大的诊断性能. 这些人工智能模型准确地识别疾病特征,并在病理分析的外部验证中优于传统方法.
科学领域:
- 脏病理学 脏病理学
- 计算病理学计算病理学
- 医学中的人工智能.
背景情况:
- 活检的组织学分析对于诊断脏疾病和预测结果至关重要.
- 在大型数据集上预训练的病理学基础模型,为下游任务提供了有希望的性能.
- 将这些模型与多个实例学习 (MIL) 集成为病理学分析,需要进行调查.
研究的目的:
- 评估病理学基础模型与MIL结合用于病理学分析的实用性.
- 将基础模型的诊断性能与传统的ResNet50模型进行比较.
- 评估模型对外部数据集进行概括和预测临床结果的能力.
主要方法:
- 从KPMP和JP-AID中使用了242个H&E染色完整幻灯片图像 (WSI) 来进行内部验证.
- 包括健康对照组,急性间歇性炎和糖尿病病 (DKD) 病例.
- 执行了来自东京大学医院 (UT数据集) 的83个WSI的外部验证.
- 采用预训练的病理基础模型作为补丁编码器,将其与ImageNet预训练的ResNet50.0进行比较.
主要成果:
- 基础模型在内部验证中显著优于ResNet50 (AUROC>0.980).
- 在外部验证中,基础模型保持了高性能 (AUROC > 0.800),而ResNet50的性能下降了 (AUROC = 0.768).
- 注意热图证实了基础模型,识别了诊断相关的结构.
- 基础模型在预测DKD病例中严重蛋白尿症方面也超过了ResNet50.
结论:
- 与MIL集成的病理学基础模型在病理学中实现了强大的诊断性能.
- 这些模型表现出强大的概括能力,即使训练数据有限.
- 这些发现突出了基础模型在病诊断中对现实世界的临床应用的潜力.
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