# PRINTER:Deformation-Aware Adversarial Learning for Virtual IHC Staining with In Situ Fidelity

arXiv: https://arxiv.org/abs/2509.01214 · HTML: https://mlsanity.com/paper/2509.01214/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models](https://mlsanity.com/paper/2205.07680/) (2022)
- [Deep learning-based transformation of the H&E stain into special stains](https://mlsanity.com/paper/2008.08871/) (2020)
- [Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks](https://mlsanity.com/paper/2203.01532/) (2022)
- [Pathological Semantics-Preserving Learning for H&E-to-IHC Virtual Staining](https://mlsanity.com/paper/2407.03655/) (2024)
- [Virtual stain transfer in histology via cascaded deep neural networks](https://mlsanity.com/paper/2207.06578/) (2022)
- [Virtual histological staining of unlabeled autopsy tissue](https://mlsanity.com/paper/2308.00920/) (2023)
- [Deformation equivariant cross-modality image synthesis with paired non-aligned training data](https://mlsanity.com/paper/2208.12491/) (2022)

## Abstract (arXiv)

Tumor spatial heterogeneity analysis requires precise correlation between Hematoxylin and Eosin H&E morphology and immunohistochemical (IHC) biomarker expression, yet current methods suffer from spatial misalignment in consecutive sections, severely compromising in situ pathological interpretation. In order to obtain a more accurate virtual staining pattern, We propose PRINTER, a weakly-supervised framework that integrates PRototype-drIven content and staiNing patTERn decoupling and deformation-aware adversarial learning strategies designed to accurately learn IHC staining patterns while preserving H&E staining details. Our approach introduces three key innovations: (1) A prototype-driven staining pattern transfer with explicit content-style decoupling; and (2) A cyclic registration-synthesis framework GapBridge that bridges H&E and IHC domains through deformable structural alignment, where registered features guide cross-modal style transfer while synthesized outputs iteratively refine the registration;(3) Deformation-Aware Adversarial Learning: We propose a training framework where a generator and deformation-aware registration network jointly adversarially optimize a style-focused discriminator. Extensive experiments demonstrate that PRINTER effectively achieves superior performance in preserving H&E staining details and virtual staining fidelity, outperforming state-of-the-art methods. Our work provides a robust and scalable solution for virtual staining, advancing the field of computational pathology.