GAS-MIL：面向数字病理图像分析中基础模型集成的群组聚合选择多示例学习
GAS-MIL: Group-Aggregative Selection Multi-Instance Learning for Ensemble of Foundation Models in Digital Pathology Image Analysis
AI总结：
针对数字病理中单个基础模型适配诊断任务成本高的问题，提出GAS-MIL集成框架，无需手动选特征或大量微调即可整合多模型特征，在三类癌症数据集上性能优于或持平于现有方法，为病理模型部署及精准肿瘤学研究提供支撑。
摘要：
基础模型（FMs）凭借强大的通用特征提取能力，已彻底改变了计算病理学领域。但针对特定诊断任务适配并基准测试单个基础模型往往耗时且资源密集，考虑到这类模型的规模与多样性尤其如此。为解决这一挑战，我们提出群组聚合选择多示例学习（GAS-MIL），这是一个灵活的集成框架，可无缝整合多个基础模型的特征，保留其互补优势，无需手动特征选择或大量任务特定微调。在前列腺癌（PANDA）、卵巢癌（UBC-OCEAN）、乳腺癌（TCGA-BrCa）三个癌症数据集的分类任务中，GAS-MIL的性能始终优于或持平于单个基础模型及已有的多示例学习方法，证明了其鲁棒性与泛化性。通过实现异构基础模型的高效整合，GAS-MIL简化了病理学领域的模型部署流程，也为未来多模态与精准肿瘤学应用提供了可扩展的基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
