SwinCAMF-Net: Explainable Cross-Attention Multimodal Swin Network for Mammogram Analysis.

ABSTRACT: Background: Breast cancer is a leading cause of cancer-related mortality among women, and earlier diagnosis significantly improves treatment outcomes. However, traditional mammography-based systems rely on single-modality image analysis and lack integration of volumetric and clinical context, which limits diagnostic robustness. Deep learning models have shown promising results in identification but are typically restricted to 2D feature extraction and lack cross-modal reasoning capability. Objective: This study proposes SwinCAMF-Net, a multimodal cross-attention Swin transformer network designed to improve joint breast lesion classification and segmentation by integrating multi-view mammography, 3D ROI volumes, and clinical metadata. Methods: SwinCAMF-Net employs a Swi

SUBMITTER: Narayanam LPRS 

PROVIDER: S-EPMC12691769 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature