USIGAN: Unbalanced Self-Information Feature Transport for Weakly Paired Image IHC Virtual Staining
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
Immunohistochemical (IHC) virtual staining is a task that generates virtual
IHC images from H\&E images while maintaining pathological semantic consistency
with adjacent slices. This task aims to achieve cross-domain mapping between
morphological structures and staining patterns through generative models,
providing an efficient and cost-effective solution for pathological analysis.
However, under weakly paired conditions, spatial heterogeneity between adjacent
slices presents significant challenges. This can lead to inaccurate one-to-many
mappings and generate results that are inconsistent with the pathological
semantics of adjacent slices. To address this issue, we propose a novel
unbalanced self-information feature transport for IHC virtual staining, named
USIGAN, which extracts global morphological semantics without relying on
positional correspondence.By removing weakly paired terms in the joint marginal
distribution, we effectively mitigate the impact of weak pairing on joint
distributions, thereby significantly improving the content consistency and
pathological semantic consistency of the generated results. Moreover, we design
the Unbalanced Optimal Transport Consistency (UOT-CTM) mechanism and the
Pathology Self-Correspondence (PC-SCM) mechanism to construct correlation
matrices between H\&E and generated IHC in image-level and real IHC and
generated IHC image sets in intra-group level.. Experiments conducted on two
publicly available datasets demonstrate that our method achieves superior
performance across multiple clinically significant metrics, such as IoD and
Pearson-R correlation, demonstrating better clinical relevance.
