# USIGAN: Unbalanced Self-Information Feature Transport for Weakly Paired Image IHC Virtual Staining

arXiv: https://arxiv.org/abs/2507.05843 · HTML: https://mlsanity.com/paper/2507.05843/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 60 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 30 of 31)

- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Denoising Diffusion Bridge Models](https://mlsanity.com/paper/2309.16948/) (2023)
- [Unpaired Image-to-Image Translation via Neural Schr"odinger Bridge](https://mlsanity.com/paper/2305.15086/) (2023)
- [Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue](https://mlsanity.com/paper/1803.11293/) (2018)
- [The Spatially-Correlative Loss for Various Image Translation Tasks](https://mlsanity.com/paper/2104.00854/) (2021)
- [BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix](https://mlsanity.com/paper/2204.11425/) (2022)
- [Deep learning-based transformation of the H&E stain into special stains](https://mlsanity.com/paper/2008.08871/) (2020)
- [Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2108.04547/) (2021)
- [Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks](https://mlsanity.com/paper/2203.01532/) (2022)
- [Unbalanced Feature Transport for Exemplar-based Image Translation](https://mlsanity.com/paper/2106.10482/) (2021)
- [QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation](https://mlsanity.com/paper/2203.08483/) (2022)
- [Adaptive Supervised PatchNCE Loss for Learning H&E-to-IHC Stain Translation with Inconsistent Groundtruth Image Pairs](https://mlsanity.com/paper/2303.06193/) (2023)
- [Scaling Algorithms for Unbalanced Transport Problems](https://mlsanity.com/paper/1607.05816/) (2016)
- [Contrastive Learning Improves Model Robustness Under Label Noise](https://mlsanity.com/paper/2104.08984/) (2021)
- [Modulated Contrast for Versatile Image Synthesis](https://mlsanity.com/paper/2203.09333/) (2022)
- [Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning](https://mlsanity.com/paper/2112.05240/) (2021)
- [Pathological Semantics-Preserving Learning for H&E-to-IHC Virtual Staining](https://mlsanity.com/paper/2407.03655/) (2024)
- [Marginal Contrastive Correspondence for Guided Image Generation](https://mlsanity.com/paper/2204.00442/) (2022)
- [Predicting Ki67, ER, PR, and HER2 Statuses from H&E-stained Breast Cancer Images](https://mlsanity.com/paper/2308.01982/) (2023)
- [Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning](https://mlsanity.com/paper/2406.01072/) (2024)
- [VIMs: Virtual Immunohistochemistry Multiplex staining via Text-to-Stain Diffusion Trained on Uniplex Stains](https://mlsanity.com/paper/2407.19113/) (2024)
- [Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency](https://mlsanity.com/paper/2502.13572/) (2025)
- [Rethinking the Paradigm of Content Constraints in Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2211.10867/) (2022)
- [RegWSI: Whole Slide Image Registration using Combined Deep Feature- and Intensity-Based Methods: Winner of the ACROBAT 2023 Challenge](https://mlsanity.com/paper/2404.13108/) (2024)
- [Hybrid Spiking Vision Transformer for Object Detection with Event Cameras](https://mlsanity.com/paper/2505.07715/) (2025)
- [Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion](https://mlsanity.com/paper/2412.11106/) (2024)
- [HistDiST: Histopathological Diffusion-based Stain Transfer](https://mlsanity.com/paper/2505.06793/) (2025)

## Built on by (most cited first, 3 of 3)

- [Topology-aware Pathological Consistency Matching for Weakly-Paired IHC Virtual Staining](https://mlsanity.com/paper/2601.02806/) (2026)
- [ContiStain: Cross-Domain Relation-Preserving Distillation for Continual Multi-Domain Virtual IHC Staining](https://mlsanity.com/paper/2607.03851/) (2026)
- [Towards Reliable Stain Transfer: An Iterative Data-Model Co-Optimization Framework Based on Multimodal Expert-Guided Assessment](https://mlsanity.com/paper/2607.25393/) (2026)

## Abstract (arXiv)

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.