GAS-MIL: Group-Aggregative Selection Multi-Instance Learning for Ensemble of Foundation Models in Digital Pathology Image Analysis
This incremental method streamlines model deployment for pathology by enabling efficient integration of heterogeneous foundation models, potentially benefiting researchers and clinicians in precision oncology.
The paper tackled the challenge of adapting and benchmarking multiple foundation models for specific diagnostic tasks in digital pathology by introducing GAS-MIL, an ensemble framework that integrates features from multiple models without manual selection or fine-tuning, achieving superior or on-par performance across three cancer datasets (PANDA, UBC-OCEAN, TCGA-BrCa).
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.