Foundation Model Ensemble for Out-of-Distribution Generalization: Predicting Lymph Node Metastasis in Early Gastric Cancer Using Whole-Slide Imaging

Woojin Chung, Yujun Park, Yoonho Nam

Keywords: Whole-Slide Imaging (WSI), Foundation Model, Foundation Model Ensemble, Lymph Node Metastasis Prediction, Early Gastric Cancer

TL;DR: This study examines foundation model ensembles for improving lymph node metastasis prediction in early gastric cancer using whole-slide imaging, tested on out-of-distribution datasets from external institutions and endoscopic submucosal dissection.

Abstract: Recent advances in deep learning have improved the practicality of automated analysis for whole-slide imaging. However, challenges remain in image analysis due to variations in imaging equipment, tissue preparation, staining protocols, and other variables. These variations hinder the generalizability of trained models to external datasets. Recently, foundation models trained on large-scale pathology datasets have been introduced by various research groups, demonstrating the potential to address this issue. Since each foundation model was trained on datasets collected from different sources under varying settings, the learned representations reflect different characteristics to some extent. These differences suggest that leveraging the information of multiple models could improve generalization and robustness compared to using a single model. In this study, we investigate foundation model ensembles for predicting lymph node metastasis in early gastric cancer across three different datasets. By comparing ensemble models with individual ones, we demonstrate that ensembling multiple foundation models improves performance in whole-slide imaging for both in-distribution and out-of-distribution data.

Primary Subject Area: Foundation Models

Secondary Subject Area: Application: Histopathology

Paper Type: Validation or Application

Registration Requirement: Yes

Reproducibility: https://github.com/goglxych97/MIDL25_255-Foundation_Model_Ensemble_for_Generalization.git

Visa & Travel: Yes

Midl Latex Submission Checklist: Ensure no LaTeX errors during compilation., Created a single midl25_NNN.zip file with midl25_NNN.tex, midl25_NNN.bib, all necessary figures and files., Includes \documentclass{midl}, \jmlryear{2025}, \jmlrworkshop, \jmlrvolume, \editors, and correct \bibliography command., Did not override options of the hyperref package, Did not use the times package., All authors and co-authors are correctly listed with proper spelling and avoid Unicode characters., Author and institution details are de-anonymized where needed. All author names, affiliations, and paper title are correctly spelled and capitalized in the biography section., References must use the .bib file. Did not override the bibliographystyle defined in midl.cls. Did not use \begin{thebibliography} directly to insert references., Tables and figures do not overflow margins; avoid using \scalebox; used \resizebox when needed., Included all necessary figures and removed *unused* files in the zip archive., Removed special formatting, visual annotations, and highlights used during rebuttal., All special characters in the paper and .bib file use LaTeX commands (e.g., \'e for é)., Appendices and supplementary material are included in the same PDF after references., Main paper does not exceed 9 pages; acknowledgements, references, and appendix start on page 10 or later.

Submission Number: 255