Year: 2026
Type: article
Abstract: Histopathology is essential for cancer diagnosis and treatment selection, and pathology foundation models learn visual representations from whole-slide images (WSIs). However, existing foundation models are trained on disparate datasets with varying strategies, leading to inconsistent performance and limited generalizability. Here, we introduce ELF (Ensemble Learning of Foundation models), which integrates five pretrained pathology foundation models into unified slide-level representations. Trained on 53,699 WSIs spanning 20 anatomical sites, ELF leverages ensemble learning to capture complementary information across models. ELF's slide-level architecture is designed for data-efficient downstream evaluation, including settings with limited data such as therapeutic response prediction. We evaluate ELF for disease classification and biomarker detection, as well as anticancer and immunotherapy response prediction across multiple cancer types. ELF achieves higher performance than the evaluated constituent and slide-level foundation models across the tested tasks, supporting further evaluation of ensemble learning for pathology applications in oncology.
Source: Cancer Cell
Authors: Xiangde Luo, Xiyue Wang, Feyisope Eweje, Xiaoming Zhang, Juan Luis Gomez Marti, Sarah Cascarino, Sen Yang, Yuchen Li, Ryan Quinton, Jinxi Xiang, Yuanfeng Ji, Zhe Li, Yijiang Chen, Colin Bergstrom, Ted Kim, Francesca Maria Olguin, Kelley Yuan, Matthew Abikenari, Andrew Heider, Sierra Willens, Sanjeeth Rajaram, Robert West, Joel Neal, Adam Schoenfeld, Maximilian Diehn, Chad Vanderbilt, Ruijiang Li
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $10,400
OpenAlex ID: W7212141603