Patch-Wise Hypergraph Contrastive Learning with Dual Normal Distribution Weighting for Multi-Domain Stain Transfer
Journal:
arXiv
Published Date:
Mar 12, 2025
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.
