用分组聚合选优法，高效融合多个基础模型提升病理图像分析性能。
GAS-MIL: Group-Aggregative Selection Multi-Instance Learning for Ensemble of Foundation Models in Digital Pathology Image Analysis
- 通过分组聚合机制自动选择最优特征组合，无需人工调参。
- 在前列腺、卵巢、乳腺三种癌症数据集上表现优于或持平单个模型。
- 适合需要快速集成多模型的病理诊断与精准肿瘤学研究者。
基础模型（FMs）已革新计算病理学，提供强大且通用的特征提取能力。然而，针对特定诊断任务适配和评估单个基础模型通常耗时且资源密集，尤其因其规模庞大和多样性。为应对这一挑战，我们提出分组聚合选择多实例学习（GAS-MIL），一种灵活的集成框架，可无缝融合多个基础模型的特征，保留其互补优势，且无需手动特征选择或大量任务定制微调。在前列腺（PANDA）、卵巢（UBC-OCEAN）和乳腺（TCGA-BrCa）三个癌症数据集的分类任务中，GAS-MIL始终取得优于或相当的表现，证明其鲁棒性与泛化能力。该方法实现了异构基础模型的高效集成，简化了病理模型部署，并为未来多模态与精准肿瘤学应用奠定可扩展基础。
原文摘要 · Abstract (English)
Foundation models (FMs) have transformed computational pathology by providing powerful, general-purpose feature extractors. However, adapting and benchmarking individual FMs for specific diagnostic tasks is often time-consuming and resource-intensive, especially given their scale and diversity. To address this challenge, we introduce Group-Aggregative Selection Multi-Instance Learning (GAS-MIL), a flexible ensemble framework that seamlessly integrates features from multiple FMs, preserving their complementary strengths without requiring manual feature selection or extensive task-specific fine-tuning. Across classification tasks in three cancer datasets-prostate (PANDA), ovarian (UBC-OCEAN), and breast (TCGA-BrCa)-GAS-MIL consistently achieves superior or on-par performance relative to individual FMs and established MIL methods, demonstrating its robustness and generalizability. By enabling efficient integration of heterogeneous FMs, GAS-MIL streamlines model deployment for pathology and provides a scalable foundation for future multimodal and precision oncology applications.
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