A segmentation model that pools vector-quantized features via optimal transport to learnable references reports state-of-the-art Dice scores on Synapse (82.23) and ACDC (91.44) benchmarks.
Contrastive learning of global and local features for medical image segmentation with limited annotations,
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L2GNet: Optimal Local-to-Global Representation of Anatomical Structures for Generalized Medical Image Segmentation
A segmentation model that pools vector-quantized features via optimal transport to learnable references reports state-of-the-art Dice scores on Synapse (82.23) and ACDC (91.44) benchmarks.