Ensemble learning of foundation models for precision oncology

TL;DR

ELF is a novel ensemble framework that combines multiple pathology foundation models to improve accuracy and robustness in AI-driven precision oncology across various clinical applications.

Contribution

The paper introduces ELF, an ensemble learning framework that unifies multiple foundation models for pathology, enhancing performance and generalizability in clinical oncology tasks.

Findings

ELF outperforms individual models in disease classification and biomarker detection.

ELF demonstrates high robustness across multiple cancer types and therapies.

ELF is effective even with limited data in clinical settings.

Abstract

Histopathology is essential for disease diagnosis and treatment decision-making. Recent advances in artificial intelligence (AI) have enabled the development of pathology foundation models that learn rich visual representations from large-scale whole-slide images (WSIs). However, existing models are often trained on disparate datasets using varying strategies, leading to inconsistent performance and limited generalizability. Here, we introduce ELF (Ensemble Learning of Foundation models), a novel framework that integrates five state-of-the-art pathology foundation models to generate unified slide-level representations. Trained on 53,699 WSIs spanning 20 anatomical sites, ELF leverages ensemble learning to capture complementary information from diverse models while maintaining high data efficiency. Unlike traditional tile-level models, ELF's slide-level architecture is particularly advantageous in clinical contexts where data are limited, such as therapeutic response prediction. We evaluated ELF across a wide range of clinical applications, including disease classification, biomarker detection, and response prediction to major anticancer therapies, cytotoxic chemotherapy, targeted therapy, and immunotherapy, across multiple cancer types. ELF consistently outperformed all constituent foundation models and existing slide-level models, demonstrating superior accuracy and robustness. Our results highlight the power of ensemble learning for pathology foundation models and suggest ELF as a scalable and generalizable solution for advancing AI-assisted precision oncology.