Multi-modal Feature Selection and Pareto-Based Model Fusion for Breast Cancer Computer-Aided Diagnosis.

Deep learning has been widely applied to breast cancer imaging analysis, yet most existing studies optimize individual metrics such as the area under the receiver operating characteristic curve (AUC) while overlooking feature acquisition cost, feature redundancy, and prediction reliability. This paper proposes a computer-aided diagnosis method based on feature-group multi-objective optimization and Pareto model fusion. The method jointly maximizes AUC and minimizes the number of feature groups, and dynamically fuses Pareto sub-models by integrating predictive performance, feature-group count, and predictive uncertainty, yielding patient-level benign/malignant probabilities with uncertainty estimates. Experiments on clinical data and digital breast tomosynthesis (DBT) images from The Cancer Imaging Archive (TCIA) demonstrate that the proposed method reduces the number of feature groups while maintaining diagnostic performance and provides a quantitative basis for evaluating prediction reliability and stability.