Breast cancer classification based on microcalcifications using dual branch vision transformer fusion.

Breast microcalcifications (MCs) are among the earliest and most challenging indicators of breast cancer, owing to their subtle morphological characteristics and the structural variability between CC and MLO mammographic projections. Current deep learning architectures frequently lose micro-level lesion details while attempting to model cross-view continuity, limiting their reliability in clinical diagnosis. This paper introduces a novel anatomy-aware Multiview transformer architecture designed to preserve microcalcification-scale texture patterns and enforce cross-view anatomical consistency. This approach enables a degree of view-coherent feature learning not previously achieved in mammography analysis. A key innovation is the development of pathology-oriented, view-congruent transformer representations that maintain the fine morphological features of MCs and preserve structural integrity across projections, directly addressing long-standing challenges in dual-view interpretation. The proposed framework was evaluated on the CBIS-DDSM dataset and demonstrated strong diagnostic performance, achieving an accuracy of 96.80, a precision of 97.10, a recall of 96.40, an F1-score of 96.70, and an AUC of 0.982. It also performed effectively in clinically challenging subgroups, obtaining the highest class-wise F1-scores for No Damage (0.9792), Matrix Cracking (0.9565), and Pleomorphic MCs (0.9608). Moreover, visual explanation techniques consistently highlighted clinically relevant calcification clusters, enhancing both interpretability and clinical reliability. These results establish a new representational paradigm for dual-view mammography. The proposed framework advances automated microcalcification assessment by integrating fine-grained lesion preservation with anatomically aligned Multiview reasoning.