Breast　cancer　is　one　of　the　most　prevalent　cancers　among　women,　with　early　detection　playing　a　critical　role　in　improving　survival　rates.　This　study　introduces　a　novel　transformer-based　explainable　model　for　breast　cancer　lesion　segmentation　(TEBLS),　aimed　at　enhancing　the　accuracy　and　interpretability　of　breast　cancer　lesion　segmentation　in　medical　imaging.　TEBLS　integrates　a　multi-scale　information　fusion　approach　with　a　hierarchical　vision　transformer,　capturing　both　local　and　global　features　by　leveraging　the　self-attention　mechanism.　This　model　addresses　the　limitations　of　existing　segmentation　methods,　such　as　the　inability　to　effectively　capture　long-range　dependencies　and　fine-grained　semantic　information.　Additionally,　TEBLS　incorporates　visualization　techniques　to　provide　insights　into　the　segmentation　process,　enhancing　the　model＇s　interpretability　for　clinical　use.　Experiments　demonstrate　that　TEBLS　outperforms　traditional　and　existing　deep　learning-based　methods　in　segmenting　complex　breast　cancer　lesions　with　variations　in　size,　shape,　and　texture,　achieving　a　mean　DSC　of　81.86%　and　a　mean　AUC　of　97.72%　on　the　CBIS-DDSM　test　set.　Our　model　not　only　improves　segmentation　accuracy　but　also　offers　a　more　explainable　framework,　which　has　the　potential　to　be　used　in　clinical　settings.