Journal of Zhejiang University

ENGINEERING Information Technology & Electronic Engineering 2026 Vol.27 No.10 P.1-11

http://doi.org/10.1631/ENG.ITEE.2026.0118

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

Author(s): Jianqiao YE, Jiahong SUN, Jiandong WANG, Xinyang LI, Puti YAN, Xingye CHEN
Affiliation(s): 1. National College for Excellent Engineers, Beihang University, Beijing 100191, China more
Corresponding email(s): chenxingye@buaa.edu.cn
Key Words: Virtual staining, Unsupervised learning, Schrödinger bridge, Digital pathology, Image processing

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Jianqiao YE, Jiahong SUN, Jiandong WANG, Xinyang LI, Puti YAN, Xingye CHEN. High-fidelity unsupervised virtual staining for digital pathology via progressive Schrödinger bridge dynamics[J]. Journal of Zhejiang University Science C, 2026, 27(10): 1-11.

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author="Jianqiao YE, Jiahong SUN, Jiandong WANG, Xinyang LI, Puti YAN, Xingye CHEN",
journal="Journal of Zhejiang University Science C",
volume="27",
number="10",
pages="1-11",
year="2026",
publisher="Zhejiang University Press & Springer",
doi="10.1631/ENG.ITEE.2026.0118"
}

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%A Jianqiao YE
%A Jiahong SUN
%A Jiandong WANG
%A Xinyang LI
%A Puti YAN
%A Xingye CHEN
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%D 2026
%I Zhejiang University Press & Springer
%DOI 10.1631/ENG.ITEE.2026.0118

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A1 - Jianqiao YE
A1 - Jiahong SUN
A1 - Jiandong WANG
A1 - Xinyang LI
A1 - Puti YAN
A1 - Xingye CHEN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 27
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SP - 1
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%@ 1869-1951
Y1 - 2026
PB - Zhejiang University Press & Springer
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DOI - 10.1631/ENG.ITEE.2026.0118

Abstract
Chinese Summary
Academic Network
Reviewer Comment

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.

基于渐进薛定谔桥动力学的数字病理高保真无监督虚拟染色

叶剑桥^1,2，孙嘉鸿³，王建东³，李欣阳⁴，阎菩提²，陈星晔²
¹北京航空航天大学国家卓越工程师学院，中国北京市，100191
²北京航空航天大学电子信息工程学院，中国北京市，100191
³中国人民解放军总医院普通外科医学部，中国北京市，100853
⁴清华大学人工智能学院，中国北京市，100084
摘要：虚拟染色技术可将无标记自发荧光（AF）图像转化为类苏木精-伊红（H&E）明场对比度图像，实现无损病理检测。本研究将非配对神经薛定谔桥（UNSB）框架应用于自发荧光图像向H&E图像的虚拟染色任务，并构建面向病理图像转换的增强型UNSB（E-UNSB）模型。该框架继承了UNSB非配对、单向、渐进随机传输的建模形式，同时引入病理专属结构正则化约束，以保留细胞形态与组织结构。显著性约束和全局特征一致性提供了多尺度结构引导策略，以缓解染色图像生成过程中的失真。实验结果表明，相较于现有已评估的无监督基准方法，该模型在分布相似性和形态计量一致性方面均有提升。下游定量分析进一步证实，虚拟H&E图像能够保留病理评估所需的形态计量特征，可为高通量多模态组织分析提供支撑。

关键词：虚拟染色；无监督学习；薛定谔桥；数字病理；图像处理

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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