W. Chu

Risk-Routed Implicit Boundary Refinement for Robust Ultrasound Image Segmentation

E evaluation on nine US datasets covering lymph nodes, breast lesions, thyroid nodules, and prostate shows that RIBR achieves the best overall macro-average and consistently reduces boundary error across grouped and organ-specific comparisons under a compact parameter budget, suggesting that controlled implicit residual learning is a practical strategy for resource-constrained and boundary-sensitive US segmentation.