STNHCL uses hypergraph modeling of patch relationships and dual Gaussian weighting of negative samples to achieve state-of-the-art multi-domain stain transfer while addressing limitations of cycle consistency.
Vision hgnn: An image is more than a graph of nodes
2 Pith papers cite this work. Polarity classification is still indexing.
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UHR-Net proposes uncertainty-aware instance contrastive pretraining and an entropy-guided hypergraph refinement block to achieve consistent segmentation gains on five medical image benchmarks.
citing papers explorer
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Patch-Wise Hypergraph Contrastive Learning with Dual Normal Distribution Weighting for Multi-Domain Stain Transfer
STNHCL uses hypergraph modeling of patch relationships and dual Gaussian weighting of negative samples to achieve state-of-the-art multi-domain stain transfer while addressing limitations of cycle consistency.
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UHR-Net: An Uncertainty-Aware Hypergraph Refinement Network for Medical Image Segmentation
UHR-Net proposes uncertainty-aware instance contrastive pretraining and an entropy-guided hypergraph refinement block to achieve consistent segmentation gains on five medical image benchmarks.