DiM adds an f-divergence alignment loss between labeled and unlabeled logits to SAM-based semi-supervised training, claiming state-of-the-art results on two medical segmentation benchmarks.
Bidirectional copy-paste for semi-supervised medical image segmentation
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DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation
DiM adds an f-divergence alignment loss between labeled and unlabeled logits to SAM-based semi-supervised training, claiming state-of-the-art results on two medical segmentation benchmarks.