We present an AI-powered mammography support system integrating a proprietary image standardization pipeline with MoEF architecture. The system unifies clinical labels into a continuous 1–100 risk scale (R²=0.90), fusing DenseNet201, GCViT, and ConvNeXt models via a Multi-task Feature Fusion Adapter. It delivers end-to-end support including risk scoring, density classification, lesion detection, and auto BI-RADS reporting, validated by AUROC 0.9946, specificity 0.9756, and NPV 0.9986.

Vs. Lunit (AUROC 96%), iCAD, & Hologic (Western-trained), our system delivers 3 breakthroughs. Our Fat-center Normalization addresses Taiwan＇s 60% high breast density competitors overlook. Our MoEF architecture achieves AUROC 0.9799 (domestic) & 0.9974 (validation), surpassing Lunit with less training data. The platform auto-fills Taiwan＇s 3 mandatory MOHW forms & generates parallel ACR reports — a workflow gap no global competitor addresses.

The mammography AI market ($940M, APAC fastest-growing) is dominated by Western models (Lunit, iCAD, Hologic) lacking localization for Asia＇s dense breast tissue & workflows. Our system achieves AUROC 0.9974 (vs. Lunit＇s 0.96), auto-fills Taiwan＇s 3 mandatory MOHW forms, & supports mobile POC deployment. This builds localization barriers competitors cannot replicate, with clear expansion potential across dense breast populations in SE Asia.