Ensemble-MIL: deep learning-based ensemble framework for biomarker prediction from histopathological images in colorectal cancer
Journal
Biomedical Signal Processing and Control
Journal Volume
119
Start Page
109759
ISSN
1746-8094
Date Issued
2026-06
Author(s)
Tien, Geng-Yun
Chen, Yu-Chia
Chuang, Eric Y.
Chen, Hsiang-Han
Abstract
Recent 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.
Subjects
Biomarker prediction
Colorectal cancer
Deep learning
Histopathological image
Publisher
Elsevier BV
Description
Article number 109759
Type
journal article