In　the　present　investigation,　we　propose　an　advanced　multimodal　breast　cancer　segmentation　framework,　designated　as　DCTNet,　which　harnesses　the　synergistic　capabilities　of　Convolutional　Neural　Networks　(CNN)　and　Transformers.　This　innovative　approach　is　designed　to　amalgamate　and　utilize　diverse　informational　and　feature-rich　inputs　from　varied　modalities,　significantly　refining　the　precision　of　automated　delineation　in　breast　cancer　lesions.　DCTNet　incorporates　dual　CNN-based　feature　learning　architectures　to　independently　assimilate　modality-specific　features,　concurrently　minimizing　cross-modality　interference　through　an　intricately　structured　encoder-decoder　mechanism　complemented　by　skip　connections.　Furthermore,　we　introduce　a　Transformer-based　encoder　dedicated　to　cross-modal　shared　learning,　adept　at　extracting　cohesive　representations　from　multimodal　inputs.　These　are　seamlessly　integrated　with　modality-specific　features　via　a　Cross-Modal　Feature　Fusion　Module　(CFM),　thereby　optimizing　the　feature　representation　through　the　CNN　decoder　pathway　for　superior　segmentation　outcomes.　Rigorous　experimental　evaluations　conducted　on　the　DCI　breast　cancer　dataset　affirm　DCTNet’s　capacity　to　either　match　or　excel　beyond　the　segmentation　efficacy　of　prevailing　advanced　multimodal　models.　This　exploration　not　only　elucidates　the　efficacy　and　indispensability　of　integrating　CNN　with　Transformer　structures,　the　cross-modal　feature　fusion　module,　and　multimodal　contrastive　loss　in　elevating　the　accuracy　of　breast　cancer　segmentation　but　also　pioneers　new　directions　for　ensuing　research　in　multimodal　medical　image　analysis.　©　2024　ACM.