A Global–Local Multi-View Mammography Diagnostic Model Integrating Swin Transformers and Wavelet CNNs

Abstract

\abstractBreast cancer is one of the leading causes of cancer-related mortality among women worldwide. Early detection through mammography screening remains critical for improving survival, yet clinical interpretation of mammograms is challenging. Deep learning approaches have been applied to assist radiologists, but CNN-based models often fail to capture global anatomical context, and Transformer-only methods tend to miss fine lesion details.\textbf{Purpose:} To develop a unified global–local multi-view hybrid mammography diagnosis model that improves diagnostic accuracy by combining complementary feature representations.\textbf{Methods:} We propose a hybrid framework that integrates Swin Transformer modules for global context modeling with Wavelet-CNN blocks for fine-grained lesion feature extraction. Each of the four standard mammography views (Left Craniocaudal, Left Mediolateral Oblique, Right Craniocaudal, and Right Mediolateral Oblique) was processed independently. A hierarchical fusion strategy was then applied: global and local features are first fused within each view and subsequently aggregated across views using an attention-based pooling mechanism. The model was optimized end-to-end with a composite loss function that supervises the global, local, and cross-view fused representations simultaneously. In addition, a CAM-based sparsity regularization was introduced to enhance lesion-focused interpretability, and a weakly supervised learning strategy was adopted to reduce annotation cost by requiring only image-level labels. The method was evaluated on two large public datasets, VinDr-Mammo and CBIS-DDSM, using AUC, accuracy, and F1-score as evaluation metrics.\textbf{Results:} The proposed model achieved AUC scores of 0.816 and 0.803 on VinDr-Mammo and CBIS-DDSM, respectively, outperforming multiple state-of-the-art comparison models. It also demonstrated superior accuracy and F1-scores on VinDr-Mammo. Saliency map visualizations showed that the model consistently highlighted clinically relevant lesion regions.\textbf{Conclusion:} This work demonstrates that combining Transformer-based global context modeling, Wavelet-CNN local feature analysis, and multi-view fusion leads to improved mammography classification performance. The proposed model provides a robust and interpretable framework that has the potential to enhance clinical decision-making and improve early breast cancer detection in large-scale screening programs.\end{abstract}