AI RESEARCH
Patch-Wise Hypergraph Contrastive Learning with Dual Normal Distribution Weighting for Multi-Domain Stain Transfer
arXiv CS.CV
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ArXi:2503.09523v2 Announce Type: replace Virtual stain transfer leverages computer-assisted technology to transform the histochemical staining patterns of tissue samples into other staining types. However, existing methods often lose detailed pathological information due to the limitations of the cycle consistency assumption. To address this challenge, we propose STNHCL, a hypergraph-based patch-wise contrastive learning method. STNHCL captures higher-order relationships among patches through hypergraph modeling, ensuring consistent higher-order topology between input and output images.