High-fidelity unsupervised virtual staining for digital pathology via progressive Schrödinger bridge dynamics

Jianqiao YE; Jiahong SUN; Jiandong WANG; Xinyang LI; Puti YAN; Xingye CHEN

doi:10.1631/ENG.ITEE.2026.0118

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High-fidelity unsupervised virtual staining for digital pathology via progressive Schrödinger bridge dynamics

Regular Papers | Updated：2026-11-05

- High-fidelity unsupervised virtual staining for digital pathology via progressive Schrödinger bridge dynamics
- High-fidelity unsupervised virtual staining for digital pathology via progressive Schrödinger bridge dynamics
- ENGINEERING Information Technology & Electronic Engineering Vol. 27, Issue 10, Pages: 1-11(2026)
- Affiliations：
  
  National College for Excellent Engineers, Beihang University, Beijing 100191, China
  School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
  Senior Department of General Surgery, Chinese PLA General Hospital, Beijing 100853, China
  College of AI, Tsinghua University, Beijing 100084, China
- Author bio：
  
  ✉ Xingye CHEN, chenxingye@buaa.edu.cn
- Funds：
- DOI：10.1631/ENG.ITEE.2026.0118
  CLC： TP391
- Received：24 April 2026，
  
  Revised：2026-08-06，
  
  Online First：06 October 2026，
  
  Published：01 October 2026
- Accepted：
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Jianqiao YE, Jiahong SUN, Jiandong WANG, et al. High-fidelity unsupervised virtual staining for digital pathology via progressive Schrödinger bridge dynamics[J]. ENGINEERING Information Technology & Electronic Engineering, 2026, 27(10): 1-11.
DOI：

Jianqiao YE, Jiahong SUN, Jiandong WANG, et al. High-fidelity unsupervised virtual staining for digital pathology via progressive Schrödinger bridge dynamics[J]. ENGINEERING Information Technology & Electronic Engineering, 2026, 27(10): 1-11. DOI： 10.1631/ENG.ITEE.2026.0118.

摘要

Abstract

Virtual staining converts label-free autofluorescence (AF) images into hematoxylin and eosin (H&E)-like brightfield contrast for nondestructive pathology. We adapt the unpaired neural Schrödinger bridge (UNSB) framework to AF-to-H&E virtual staining and develop an enhanced UNSB (E-UNSB) model for pathology-oriented image translation. E-UNSB retains the unpaired

unidirectional

and progressive stochastic transport formulation of UNSB while introducing pathology-specific structural regularization to preserve cellular morphology and tissue architecture. In particular

saliency constraints and global feature consistency provide multi-scale structural guidance to reduce distortion during staining generation. Experiments demonstrate improved distributional similarity and morphometric consistency over the evaluated unsupervised baselines. Quantitative downstream analysis further indicates that virtual H&E images retain morphometric features relevant to pathological assessment

supporting their potential for high-throughput multimodal tissue analysis.

关键词

Keywords

references

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