MammoDenseSegNet outperforms VGG16 baseline for dense tissue segmentation across mammography devices

New encoder-decoder CNN (MammoDenseSegNet) segments breast dense tissue more reliably than VGG16 baseline across 3 datasets (1,499 mammograms, 606 women): Dice 0.63–0.91 vs 0.06–0.82 (p<0.001). Biggest gain in low-density breasts where baseline nearly fails (Dice 0.16→0.63).

• Retrospective diagnostic-accuracy study; 1,499 mammograms from 606 women across two public datasets (VinDR-Mammo, EMBED) and one private dataset spanning varied densities and imaging artifacts.
• Architecture combines adaptive dual-attention module (spatial + channel), multi-kernel receptive field module, and multi-scale Dice loss with deep supervision; tested against a publicly available VGG16-based segmentation algorithm.
• Key limitation: relatively small, single-sex cohort (606 women); no prospective or external multi-institutional validation beyond the three included datasets, limiting generalizability to other scanner vendors and clinical workflows.

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