# Multi-query cross-attention

Patient-level ultrasound diagnosis has relied on a single attention bottleneck to aggregate multiple views, which dilutes sparse malignant cues and optimizes only global accuracy. C2LUE replaces that bottleneck with multi-query cross-attention, letting several learnable queries extract complementary morphological evidence, and adds a differentiable sensitivity-specificity geometric-mean regularizer during training. On biopsy-proven thyroid and breast ultrasound datasets, patient-level AUC reaches 0.932 and 0.936, with sensitivity rising to 0.874 and 0.876 at a moderate cost in specificity.

Patient-level ultrasound diagnosis has relied on a single attention bottleneck to aggregate multiple views, which dilutes sparse malignant cues and optimizes only global accuracy. C2LUE replaces that bottleneck with multi-query cross-attention, letting several learnable queries extract complementary morphological evidence, and adds a differentiable sensitivity-specificity geometric-mean regularizer during training. On biopsy-proven thyroid and breast ultrasound datasets, patient-level AUC reaches 0.932 and 0.936, with sensitivity rising to 0.874 and 0.876 at a moderate cost in specificity.