TABNet combines triplet augmentation self-recovery with loss-weighted boundary-aware pseudo-labels, reaching 89.1% and 91.1% average Dice on MSCMRseg and ACDC, near fully supervised nnU-Net.
Ad- dressinginconsistentlabelingwithcrossimagematchingforscribble- based medical image segmentation
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TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation
TABNet combines triplet augmentation self-recovery with loss-weighted boundary-aware pseudo-labels, reaching 89.1% and 91.1% average Dice on MSCMRseg and ACDC, near fully supervised nnU-Net.