Ensemble learning of foundation models for precision oncology
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arXiv
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| Format: | Preprint |
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2025
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| author | Luo, Xiangde Wang, Xiyue Eweje, Feyisope Zhang, Xiaoming Yang, Sen Quinton, Ryan Xiang, Jinxi Li, Yuchen 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 Diehn, Maximilian Li, Ruijiang |
| author_facet | Luo, Xiangde Wang, Xiyue Eweje, Feyisope Zhang, Xiaoming Yang, Sen Quinton, Ryan Xiang, Jinxi Li, Yuchen 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 Diehn, Maximilian Li, Ruijiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16085 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ensemble learning of foundation models for precision oncology Luo, Xiangde Wang, Xiyue Eweje, Feyisope Zhang, Xiaoming Yang, Sen Quinton, Ryan Xiang, Jinxi Li, Yuchen 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 Diehn, Maximilian Li, Ruijiang Computer Vision and Pattern Recognition 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. |
| title | Ensemble learning of foundation models for precision oncology |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.16085 |