MGScreener: A multi-view mammography-based model optimized with active learning for breast cancer diagnosis

Breast cancer remains the leading cause of cancer-related mortality among women worldwide. Early screening and accurate subtype classification are critical for guiding clinical decision-making. In this study, we present MGScreener, an interpretable multi-view framework that integrates dual-view mammography images, including cranial-caudal (CC) and mediolateral oblique (MLO) views, with patient-level clinical data. The model incorporates an active learning strategy to address two key classification tasks: distinguishing benign from malignant breast lesions and identifying invasive ductal carcinoma (IDC) among malignant subtypes. An entropy-based uncertainty sampling method is employed to select highly informative cases from 210 unlabeled samples for prioritized annotation, substantially reducing manual labeling costs. Across two independent test sets, MGScreener-1 achieved an accuracy of 89.7 % and an ROC-AUC of 0.941 for benign-versus-malignant classification (Task 1), while MGScreener-2 achieved an accuracy of 88.2 % and an ROC-AUC of 0.884 for IDC identification (Task 2). With MGScreener assistance, radiologists improved their diagnostic accuracy by more than 10 % compared with independent reading. Overall, MGScreener offers a scalable and effective solution for precise breast cancer screening and molecular subtype classification.