Tien, Geng-YunGeng-YunTienChen, Yu-ChiaYu-ChiaChenLIANG-CHUAN LAITZU-PIN LUChuang, Eric Y.Eric Y.ChuangMONG-HSUN TSAIChen, Hsiang-HanHsiang-HanChen2026-03-042026-03-042026-0617468094https://www.scopus.com/pages/publications/105029900221https://scholars.lib.ntu.edu.tw/handle/123456789/736075Article number 109759Recent studies have explored histopathological whole slide images (WSIs) for predicting colorectal cancer (CRC) biomarkers, aiming to create cost-effective and efficient diagnostic tools. However, achieving strong predictive performance and generalizability across datasets remains a challenge. Here, we introduce a deep learning-based ensemble framework, Ensemble-MIL, designed to robustly predict key CRC biomarkers, including BRAF V600E, KRAS mutations, and MSI-H status, with improved cross-dataset performance. We employed two independent CRC datasets: TCGA-COAD for model training and internal evaluation, and CPTAC-COAD as an external test set to assess generalizability. All WSIs were preprocessed and divided into small image patches. A tumor detection model was applied to identify tumor regions, and patch-level; features were extracted via SimCLR, a contrastive learning method. These features were utilized to train three multiple instance learning (MIL) models: Att-MIL, Tran-MIL, and GNN-MIL. The models were then integrated into the final Ensemble-MIL framework. In internal testing with TCGA-COAD, the proposed method achieved area under the curve (AUC) scores of 0.90, 0.87, and 0.64 for MSI-H, BRAF, and KRAS, respectively. In external testing on CPTAC-COAD, it achieved AUCs of 0.78, 0.76, and 0.61 for MSI-H, BRAF, and KRAS, respectively, outperforming previous results. This framework offers a scalable and effective solution for image-based biomarker screening and demonstrates strong potential for clinical application, particularly in resource-limited settings. The code is available at https://github.com/chenh2lab/Ensemble-MIL.falseBiomarker predictionColorectal cancerDeep learningHistopathological imagejournal article10.1016/j.bspc.2026.1097592-s2.0-105029900221