A dual-attention U-Net++ ensemble with class-specific training and Bayesian tuning reports a weighted F1 of 0.8640 on the NBC 2025 wound and scale marker segmentation benchmark.
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Dual-Attention U-Net++ with Class-Specific Ensembles and Bayesian Hyperparameter Optimization for Precise Wound and Scale Marker Segmentation
A dual-attention U-Net++ ensemble with class-specific training and Bayesian tuning reports a weighted F1 of 0.8640 on the NBC 2025 wound and scale marker segmentation benchmark.