GAS-MIL: Group-Aggregative Selection Multi-Instance Learning for Ensemble of Foundation Models in Digital Pathology Image Analysis

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, espe cially 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 complementa ry 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 int egration of heterogeneous FMs, GAS - MIL streamlines model deployment for pathology and provides a scalable foundation for future multimodal and precision oncology applications.