Year: 2025
Type: preprint
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 using 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, 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: arXiv (Cornell University)
Authors: Luo, Xiangde, Wang, Xiyue, Eweje, Feyisope, Zhang, Xiaoming, Marti, Juan Luis Gomez, Cascarino, Sarah, Yang, Sen, Li, Yuchen, Quinton, Ryan, Xiang, Jinxi, Ji, Yuanfeng, Li, Zhe, Chen, Yijiang, Bergstrom, Colin, Kim, Ted, Olguin, Francesca Maria, Yuan, Kelley, Abikenari, Matthew, Heider, Andrew, Willens, Sierra, Rajaram, Sanjeeth, West, Robert, Neal, Joel, Schoenfeld, Adam, Diehn, Maximilian, Vanderbilt, Chad, Li, Ruijiang
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7077674529
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