Article

Toward Foundation Models in Oncology: BCP-HyEnS: A Scalable Hybrid Ensemble Integrating Biomarkers and Explainability for Breast Cancer Diagnosis and Treatment

2026-03-10

Abstract excerpt

<title>Abstract</title> <p>The emergence of large-scale foundation models in artificial intelligence promises to revolutionize disease diagnosis and treatment planning, however their translation to clinical practice faces fundamental challenges: interpretability, biomarker integration, and regulatory readiness. Addressing these gaps, we present BCP-HyEnS (Breast Cancer Predictor-Hybrid Ensemble System), a foundat...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 00c93dad-4adf-5c63-ba4d-42770110340c
DOI: 10.21203/rs.3.rs-9045752/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Toward Foundation Models in Oncology: BCP-HyEnS: A Scalable Hybrid Ensemble Integrating Biomarkers and Explainability for Breast Cancer Diagnosis and TreatmentDOI 10.21203/rs.3.rs-9045752/v1Read

Adaptive Integration of Heterogeneous Foundation Models to Find Histologically Predictable Genes in Breast CancerSemantic proximity Read

Immunotherapy Efficacy Prediction in Cancer: An Artificial Intelligence Approach with Unannotated H&E Whole-Slide ImagesSemantic proximity Read

An Ensemble-Base Machine Learning Approach to Predict 2- and 10-Year Breast CancerSemantic proximity Read

Hematologic Biomarkers and AI in Breast Cancer: A New Frontier for Risk Stratification and Treatment Response PredictionSemantic proximity Read

Established Machine Learning Matches Tabular Foundation Models in Clinical PredictionsSemantic proximity Read

Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligenceSemantic proximity Read

Integrating Gene Expression and Proteomics for Breast Cancer Biomarker Prediction through a Deep Learning Framework with SHAP-Based ExplainabilitySemantic proximity Read

Explainable AI for Precision Oncology: A Task-Specific Approach Using Imaging, Multi-omics, and Clinical DataSemantic proximity Read

Classification and Interpretation of Histopathology Images: Leveraging Ensemble of EfficientNetV1 and EfficientNetV2 ModelsSemantic proximity Read

Hematologic Biomarkers and AI in Breast Cancer: A New Frontier for Risk Stratification and Treatment Response PredictionSemantic proximity Read

Multiple instance learning with spatial transcriptomics for interpretable patient-level predictions: application in glioblastomaSemantic proximity Read

DeepTRIAGE: Interpretable and Individualised Biomarker Scores using Attention Mechanism for the Classification of Breast Cancer Sub-typesSemantic proximity Read