Multimodal microscopic imaging combining dual branch structure-component decoupled fusion for breast tumor image analysis and label-free intelligent diagnosis

Rapid and accurate analysis of breast tissue is essential for pathological studies and surgical decision support, whereas conventional frozen section pathology relies on time-consuming staining and subjective interpretation, limiting efficiency. This study introduced a multimodal fusion strategy that simultaneously integrates four complementary imaging modalities, including bright-field, phase-contrast, green fluorescence, and blue fluorescence imaging, to comprehensively capture tissue morphology, structural and molecular features. Further, a dual-branch structure-component decoupling network was designed to separately model structural and compositional information while allowing cross-branch interaction. An input-level spatial modality fusion module (SMF) was used to adaptively fuse paired modalities, while a contribution estimation module quantified structural, compositional, and coupling contributions to enable interpretable classification. The network achieved a three-class accuracy of 92.51% and an area under the curve (AUC) of 0.9852, outperforming dual-modality and tri-modality combinations. The four-modality fusion efficiently integrated structural and molecular features, enabling comprehensive tissue feature representation, and improved classification performance. This approach demonstrated that combining structural and compositional information via dual-branch decoupling and multimodal fusion allowed rapid, accurate, and interpretable tissue classification. It provided a reliable tool for tissue feature analysis and pathological assessment and shows strong potential for clinical applications.