Ensemble learning of foundation models for precision oncology

• URL: http://arxiv.org/abs/2508.16085v1
• Date: Fri, 22 Aug 2025 04:36:10 GMT
• Title: Ensemble learning of foundation models for precision oncology
• Authors: Xiangde Luo, Xiyue Wang, Feyisope Eweje, Xiaoming Zhang, Sen Yang, Ryan Quinton, Jinxi Xiang, Yuchen Li, 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, Maximilian Diehn, Ruijiang Li,
• Abstract summary: 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.<n>ELF consistently outperformed all constituent foundation models and existing slide-level models, demonstrating superior accuracy and robustness.
• Score: 19.813705315667438
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Federated and Parameter-Efficient Framework for Large Language Model Training in Medicine [59.78991974851707]
  Large language models (LLMs) have demonstrated strong performance on medical benchmarks, including question answering and diagnosis.<n>Most medical LLMs are trained on data from a single institution, which faces limitations in generalizability and safety in heterogeneous systems.<n>We introduce the model-agnostic and parameter-efficient federated learning framework for adapting LLMs to medical applications.
  arXiv  Detail & Related papers  (2026-01-29T18:48:21Z)
• Investigating the Impact of Histopathological Foundation Models on Regressive Prediction of Homologous Recombination Deficiency [52.50039435394964]
  We systematically evaluate foundation models for regression-based tasks.<n>We extract patch-level features from whole slide images (WSI) using five state-of-the-art foundation models.<n>Models are trained to predict continuous HRD scores based on these extracted features across breast, endometrial, and lung cancer cohorts.
  arXiv  Detail & Related papers  (2026-01-29T14:06:50Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• DermINO: Hybrid Pretraining for a Versatile Dermatology Foundation Model [69.20140430678092]
  DermNIO is a versatile foundation model for dermatology.<n>It incorporates a novel hybrid pretraining framework that augments the self-supervised learning paradigm.<n>It consistently outperforms state-of-the-art models across a wide range of tasks.
  arXiv  Detail & Related papers  (2025-08-17T00:41:39Z)
• Clinical NLP with Attention-Based Deep Learning for Multi-Disease Prediction [44.0876796031468]
  This paper addresses the challenges posed by the unstructured nature and high-dimensional semantic complexity of electronic health record texts.<n>A deep learning method based on attention mechanisms is proposed to achieve unified modeling for information extraction and multi-label disease prediction.
  arXiv  Detail & Related papers  (2025-07-02T07:45:22Z)
• Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imaging [26.589728923739596]
  We evaluate and compare the transferability of Convolutional Neural Networks and Foundation Models in predicting clinical outcomes in COVID-19 patients.<n>The evaluations were conducted across multiple learning paradigms, including both extensive full-data scenarios and more clinically realistic Few-Shot Learning settings.
  arXiv  Detail & Related papers  (2025-06-23T09:16:04Z)
• Are foundation models useful feature extractors for electroencephalography analysis? [9.413178499853156]
  We investigate the effectiveness of foundation models in medical time series analysis involving electroencephalography (EEG)<n>Our analysis shows that foundation models extract meaningful EEG features, outperform specialised models even without domain adaptation, and localise task-specific biomarkers.
  arXiv  Detail & Related papers  (2025-02-28T14:21:34Z)
• Molecular-driven Foundation Model for Oncologic Pathology [6.922502805825084]
  We introduce Threads, a slide-level foundation model capable of generating universal representations of whole-slide images of any size.<n> Threads was pre-trained using a multimodal learning approach on a diverse cohort of 47,171 hematoxylin and eosin (H&E)-stained tissue sections.
  arXiv  Detail & Related papers  (2025-01-28T02:35:02Z)
• Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation [41.25398139658467]
  Current foundation models have only been evaluated on a limited type and number of tasks, leaving their generalization ability and overall performance unclear.<n>We establish a benchmark to evaluate the performance of off-the-shelf foundation models across six distinct clinical task types.<n>We propose a unified knowledge distillation framework consisting of both expert and self-knowledge distillation, where the former allows the model to learn from the knowledge of multiple expert models.
  arXiv  Detail & Related papers  (2024-07-26T01:12:54Z)
• Fairness Evolution in Continual Learning for Medical Imaging [47.52603262576663]
  This study examines how bias evolves across tasks using domain-specific fairness metrics and how different CL strategies impact this evolution.<n>Our results show that Learning without Forgetting and Pseudo-Label achieve optimal classification performance, but Pseudo-Label is less biased.
  arXiv  Detail & Related papers  (2024-04-10T09:48:52Z)
• Ensembling Handcrafted Features with Deep Features: An Analytical Study for Classification of Routine Colon Cancer Histopathological Nuclei Images [13.858624044986815]
  We have used F1-measure, Precision, Recall, AUC, and Cross-Entropy Loss to analyse the performance of our approaches. We observed from the results that the DL features ensemble bring a marked improvement in the overall performance of the model.
  arXiv  Detail & Related papers  (2022-02-22T06:48:50Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• An Optimal Control Approach to Learning in SIDARTHE Epidemic model [67.22168759751541]
  We propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data. We forecast the epidemic evolution in Italy and France.
  arXiv  Detail & Related papers  (2020-10-28T10:58:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.