整合五个病理基础模型，提升癌症诊断与治疗预测精度
Ensemble learning of pathology foundation models for precision oncology
- 通过集成学习融合五个预训练病理模型，生成统一的切片级表示
- 在53,699张全切片图像上训练，多任务表现优于单个模型
- 适合数据稀缺场景，如治疗反应预测，适用于精准肿瘤学研究
组织病理学对癌症诊断和治疗选择至关重要，病理基础模型可从全切片图像（WSIs）中学习视觉表征。然而，现有基础模型基于不同数据集、采用不同策略训练，导致性能不一致且泛化能力有限。本文提出ELF（Ensemble Learning of Foundation models），将五个预训练病理基础模型整合为统一的切片级表征。ELF在涵盖20个解剖部位的53,699张WSIs上训练，利用集成学习捕获各模型间的互补信息。其切片级架构设计支持数据高效下游评估，包括治疗反应预测等数据有限场景。我们在多种癌症类型上评估了ELF在疾病分类、生物标志物检测及抗癌药和免疫治疗反应预测的表现。ELF在所有测试任务中均优于所评估的单个模型及切片级基础模型，支持进一步探索集成学习在肿瘤学病理应用中的潜力。
原文摘要 · Abstract (English)
Histopathology is essential for cancer diagnosis and treatment selection, and pathology foundation models learn visual representations from whole-slide images (WSIs). However, existing foundation models are trained on disparate datasets using varying strategies, leading to inconsistent performance and limited generalizability. Here, we introduce ELF (Ensemble Learning of Foundation models), which integrates five pretrained pathology foundation models into unified slide-level representations. Trained on 53,699 WSIs spanning 20 anatomical sites, ELF leverages ensemble learning to capture complementary information across models. ELF's slide-level architecture is designed for data-efficient downstream evaluation, including settings with limited data such as therapeutic response prediction. We evaluate ELF for disease classification, biomarker detection, as well as anticancer and immunotherapy response prediction across multiple cancer types. ELF achieves higher performance than the evaluated constituent and slide-level foundation models across the tested tasks, supporting further evaluation of ensemble learning for pathology applications in oncology.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。