UCBound-Net uses MC Dropout uncertainty maps to focus distillation, calibration, and replay on boundary regions, reducing catastrophic forgetting in domain-incremental ultrasound segmentation.
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UCBound-Net: Uncertainty-Guided Boundary-Aware Continual Learning for Domain-Incremental Ultrasound Segmentation
UCBound-Net uses MC Dropout uncertainty maps to focus distillation, calibration, and replay on boundary regions, reducing catastrophic forgetting in domain-incremental ultrasound segmentation.