Patch-Wise Hypergraph Contrastive Learning with Dual Normal Distribution Weighting for Multi-Domain Stain Transfer

• URL: http://arxiv.org/abs/2503.09523v1
• Date: Wed, 12 Mar 2025 16:39:53 GMT
• Title: Patch-Wise Hypergraph Contrastive Learning with Dual Normal Distribution Weighting for Multi-Domain Stain Transfer
• Authors: Haiyan Wei, Hangrui Xu, Bingxu Zhu, Yulian Geng, Aolei Liu, Wenfei Yin, Jian Liu,
• Abstract summary: Virtual stain transfer uses computer-assisted technology to transform the histochemical staining patterns of tissue samples into other staining types.<n>We propose STNHCL, a hypergraph-based patch-wise contrastive learning method.<n>We show that STNHCL achieves state-of-the-art performance in the two main categories of stain transfer tasks.
• Score: 2.5241344941284365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Additionally, we introduce a novel negative sample weighting strategy that leverages discriminator heatmaps to apply different weights based on the Gaussian distribution for tissue and background, thereby enhancing traditional weighting methods. Experiments demonstrate that STNHCL achieves state-of-the-art performance in the two main categories of stain transfer tasks. Furthermore, our model also performs excellently in downstream tasks. Code will be made available.

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