An Interpretable Lesion‐Aware Diagnostic Model for Full‐Field Mammography Classification

ABSTRACT Accurate diagnosis of breast cancer is critical for improving patient outcomes. Yet breast lesions are small and mammograms are high‐resolution, patch‐based methods that often ignore peritumoral context, undermining diagnostic accuracy. We therefore develop an interpretable lesion‐aware diagnostic model (LADM), which directly identifies tumor regions from full‐field mammograms to improve classification accuracy and clinical trust. LADM incorporates a hyper lesion‐aware module that combines spatial‐ and channel‐guided attention. LADM processes craniocaudal (CC) and mediolateral oblique (MLO) mammograms via three branches—CC, MLO, and dual‐view. The single‐view branches independently learn diagnostic features and perform classification, while the dual‐view branch concatenates CC and MLO features for late‐fusion prediction to exploit cross‐view complementarity. Model interpretability and lesion localization are assessed with Grad‐CAM++ heatmaps. On INBreast and CBIS‐DDSM, LADM consistently outperforms single‐view baselines. In the dual‐view setting, it achieves AUC 0.950 and accuracy 0.903 on INBreast, and AUC 0.911 and accuracy 0.848 on CBIS‐DDSM, respectively. Visualizations show that the model focuses on diagnostically relevant regions, supporting clinical interpretability. LADM learns lesion‐aware features directly on full‐field images, fuses CC‐MLO while staying single‐view robust, and offers transparent predictions with lesion maps. Together these yield accurate, interpretable classification of breast cancer.