Virtual immunohistochemistry (IHC) staining from hematoxylin and eosin (H&E) images can accelerate diagnostics by providing preliminary molecular insight directly from routine sections, reducing the need for repeat sectioning when tissue is limited. Existing methods improve realism through contrastive objectives, prototype matching, or domain alignment, yet the generator itself receives no direct guidance from pathology foundation models. We present UNIStainNet, a SPADE-UNet conditioned on dense spatial tokens from a frozen pathology foundation model (UNI), providing tissue-level semantic guidance for stain translation. A misalignment-aware loss suite preserves stain quantification accuracy, and learned stain embeddings enable a single model to serve multiple IHC markers simultaneously. On MIST, UNIStainNet achieves state-of-the-art distributional metrics on all four stains (HER2, Ki67, ER, PR) from a single unified model, where prior methods typically train separate per-stain models. On BCI, it also achieves the best distributional metrics. A tissue-type stratified failure analysis reveals that remaining errors are systematic, concentrating in non-tumor tissue. Code is available at https://github.com/facevoid/UNIStainNet.
Pathological Semantics-Preserving Learning for H&E-to-IHC Virtual Staining
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences（中国科学院大学深圳先进技术学院）；The Hong Kong Polytechnic University（香港理工大学）；School of Computer Science, University of Nottingham Ningbo China（宁波诺丁汉大学计算机学院）
ContiStain: Cross-Domain Relation-Preserving Distillation for Continual Multi-Domain Virtual IHC Staining
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）；School of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
Topology-aware Pathological Consistency Matching for Weakly-Paired IHC Virtual Staining
Guangdong Research Center of Polarization Imaging and Measurement Engineering Technology（广东极化成像与测量工程研究中心）；Shenzhen Key Laboratory for Minimal Invasive Medical Technologies（深圳微创医疗技术重点实验室）；Institute of Biopharmaceutical and Health Engineering（生物制药与健康工程研究院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际 Graduate School）；Tsinghua University（清华大学）
Histology Virtual Staining with Mask-Guided Adversarial Transfer Learning for Tertiary Lymphoid Structure Detection
Yu-Yue Pathology Research Center（渝越病理研究中心）；Jinfeng Laboratory（金凤实验室）；Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Army Medical University (Third Military Medical University)（陆军军医大学（第三军医大学）西南医院病理科及西南癌症中心）
Score-based Diffusion Model for Unpaired Virtual Histology Staining
Department of Health Technology and Informatics, Hong Kong Polytechnic University, China（香港理工大学健康科技与信息学系）；Department of Clinical Neurosciences, University of Cambridge, UK（剑桥大学临床神经科学系）；School of Science and Engineering, University of Dundee, UK（邓迪大学科学与工程学院）；Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK（剑桥大学应用数学与理论物理系）
VIMs: Virtual Immunohistochemistry Multiplex staining via Text-to-Stain Diffusion Trained on Uniplex Stains
Kahlert School of Computing, University of Utah（犹他大学卡勒特计算学院）；Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；The Catholic University of Korea College of Medicine（韩国天主教大学医学院）；Huntsman Cancer Institute, University of Utah Health（犹他大学健康亨茨曼癌症研究所）；Department of Pathology, University of Utah（犹他大学病理学系）
PGVMS: A Prompt-Guided Unified Framework for Virtual Multiplex IHC Staining with Pathological Semantic Learning
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing, China（深圳先进技术研究院，中国科学院和中国科学院大学，北京，中国）；Department of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China（中山大学肿瘤中心病理科，南方肿瘤临床研究中心，广州，中国）；Hong Kong Polytechnic University, Hong Kong SAR, China（香港理工大学，香港特别行政区，中国）；Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR 999077, China（电子工程系，香港中文大学，香港特别行政区，中国）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China（深圳先进技术研究院，中国科学院，深圳，中国）
Adaptive H&E-IHC information fusion staining framework based on feature extra
HKUST(GZ)（香港科技大学（广州））；Deep Interdisciplinary Intelligence Lab（深度交叉智能实验室）；Institute of Deep Perception Technology, JITRI（JITRI深度感知技术研究所）；Shandong University（山东大学）
Immunohistochemical (IHC) staining highlights the molecular information critical to diagnostics in tissue samples. However, compared to H&E staining, IHC staining can be much more expensive in terms of both labor and the laboratory equipment required. This motivates recent research that demonstrates that the correlations between the morphological information present in the H&E-stained slides and the molecular information in the IHC-stained slides can be used for H&E-to-IHC stain translation. However, due to a lack of pixel-perfect H&E-IHC groundtruth pairs, most existing methods have resorted …
Roche Innovation Center Munich（慕尼黑罗氏创新中心）；DeePathology Ltd.（DeePathology有限公司）
Histopathological evaluation of tissue samples is a key practice in patient diagnosis and drug development, especially in oncology. Historically, Hematoxylin and Eosin (H&E) has been used by pathologists as a gold standard staining. However, in many cases, various target specific stains, including immunohistochemistry (IHC), are needed in order to highlight specific structures in the tissue. As tissue is scarce and staining procedures are tedious, it would be beneficial to generate images of stained tissue virtually. Virtual staining could also generate in-silico multiplexing of different stai…
Diagnosising Helicobacter pylori using AutoEncoders and Limited Annotations through Anomalous Staining Patterns in IHC Whole Slide Images
Universitat Autònoma de Barcelona（巴塞罗那自治大学）；Computer Vision Center（计算机视觉中心）；Hospital Universitari General de Catalunya-Grupo QuironSalud（加泰罗尼亚综合大学医院-奎荣萨尔集团）；Institut de Recerca contra la Leucèmia Josep Carreras (IJC)（何塞普·卡雷拉斯白血病研究所）
From Pixels to Pathology: Restoration Diffusion for Diagnostic-Consistent Virtual IHC
organization= Institute of Pathology, Technical University of Munich, TUM School of Medicine；organization= Computer Aided Medical Procedures (CAMP), TU Munich , city= Munich , country= Germany；organization= Munich Center for Machine Learning (MCML) , city= Munich , country= Germany；organization= TUM School of Computation, Information；organization= Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences , city= Mashhad , country= Iran；organization= Munich Data Science Institute (MDSI) , city= Munich , country= Germany
Cross-channel Perception Learning for H&E-to-IHC Virtual Staining
College of Intelligent Robotics and Advanced Manufacturing（智能机器人与先进制造学院）；Department of Pathology, Zhongshan Hospital, Fudan University（复旦大学中山医院病理科）；Yiwu Research Institute, Fudan University（义乌研究院）；Department of Computer Science, The University of Toronto（多伦多大学计算机科学系）
Queen Mary University of London（伦敦玛丽女王大学）；Karolinska University Hospital（卡罗林斯卡大学医院）
Histopathological cancer diagnosis is based on visual examination of stained tissue slides. Hematoxylin and eosin (H\&E) is a standard stain routinely employed worldwide. It is easy to acquire and cost effective, but cells and tissue components show low-contrast with varying tones of dark blue and pink, which makes difficult visual assessments, digital image analysis, and quantifications. These limitations can be overcome by IHC staining of target proteins of the tissue slide. IHC provides a selective, high-contrast imaging of cells and tissue components, but their use is largely limited by a …
Deep learning-enabled virtual multiplexed immunostaining of label-free tissue for vascular invasion assessment
University of California, Los Angeles（加州大学洛杉矶分校）；California NanoSystems Institute（加州纳米系统研究所）；Hadassah Hebrew University Medical Center（哈达萨希伯来大学医学中心）；University of Southern California（南加州大学）
Segmentation-before-Staining Improves Structural Fidelity in Virtual IHC-to-Multiplex IF Translation
Interdisciplinary Program in Cancer Biology, Seoul National University College of Medicine（癌症生物学跨学科项目，首尔国立大学医学院）；Interdisciplinary Program in Bioengineering, Seoul National University（生物工程跨学科项目，首尔国立大学）；Department of Biomedical Sciences, Seoul National University（生物医学科学系，首尔国立大学）；Department of Radiology, Seoul National University Hospital（放射科，首尔国立大学医院）；Department of Radiology, Seoul National University College of Medicine（放射科，首尔国立大学医学院）；Healthcare AI Research Institute, Seoul National University Hospital（医疗人工智能研究 institute，首尔国立大学医院）
iSight: Towards expert-AI co-assessment for improved immunohistochemistry staining interpretation
Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania（病理与实验室医学系，佩尔曼医学院，宾夕法尼亚大学）；Department of Electrical and Systems Engineering, University of Pennsylvania（电气与系统工程系，宾夕法尼亚大学）；Department of Biomedical Data Science, Stanford University School of Medicine（生物医学数据科学系，斯坦福大学医学院）；Department of Pathology, University of Maryland School of Medicine（病理学系，马里兰大学医学院）；Department of Pathology, Stanford University School of Medicine（病理学系，斯坦福大学医学院）
The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is an essential part of the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to manually assess status and scoring of several established biomarkers, including ER, PGR, HER2 and KI67. However, this is a task that can also be facilitated by computational pathology image analysis methods. The research in computational pathology has recently made numerous substantial advances, often …
LGD-Net: Latent-Guided Dual-Stream Network for HER2 Scoring with Task-Specific Domain Knowledge
School of Mathematics and Statistics, Fujian Normal University（福建师范大学数学与统计学学院）；Department of Oncology, Mengchao Hepatobiliary Hospital of Fujian Medical University（福建医科大学 Mengchao 肝胆医院肿瘤科）
Deep Learning for Generating Computational PIN-4 Immunohistochemistry Staining from Prostate Biopsy H&E Images
Biomedical Engineering University of California, Irvine Irvine, CA, USA（生物医学工程 卡罗来纳大学伊文城分校 伊文城，加州，美国）；Laboratory Medicine Biomedical Engineering Electrical Engineering（实验室医学 生物医学工程 电气工程）；Computer Science University of California, Irvine Irvine, CA, USA（计算机科学 卡罗来纳大学伊文城分校 伊文城，加州，美国）
HistDiT: A Structure-Aware Latent Conditional Diffusion Model for High-Fidelity Virtual Staining in Histopathology
Edge Hill University（埃奇希尔大学）；University of Nottingham Malaysia（诺丁汉大学马来西亚分校）；University of Southampton Malaysia（南安普顿大学马来西亚分校）；Cancer Research Malaysia（马来西亚癌症研究中心）
Artificial Intelligence-Assisted Prostate Cancer Diagnosis for Reduced Use of Immunohistochemistry
Stavanger University Hospital（斯塔万格大学医院）；University of Stavanger（斯塔万格大学）；Karolinska Institutet（卡罗林斯卡学院）；Synlab；University of Bergen（卑尔根大学）；Griffith University（格里菲斯大学）；SciLifeLab
Automated segmentation of rheumatoid arthritis immunohistochemistry stained synovial tissue
Queen Mary University of London（伦敦玛丽女王大学）；Centre for Translational Bioinformatics（转化生物信息学中心）；Digital Environment Research Institute（数字环境研究所）；Centre for Experimental Medicine and Rheumatology（实验医学与风湿病学中心）
HAPS: Rethinking Image Similarity for Virtual Staining
Skolkovo Institute of Science and Technology（斯克洛洛沃科学与技术研究所）；University of Sharjah（沙迦大学）；National Medical Research Radiological Centre of the Ministry of Health of the Russian Federation（俄罗斯联邦卫生部国家医学研究放射中心）
CentraleSupélec, Université Paris-Saclay（巴黎萨克雷大学中央高等电力学院）；Gustave Roussy Cancer Campus（古斯塔夫·鲁西癌症校区）；Therapanacea（Therapanacea公司）
In whole slide imaging, commonly used staining techniques based on hematoxylin and eosin (H&E) and immunohistochemistry (IHC) stains accentuate different aspects of the tissue landscape. In the case of detecting metastases, IHC provides a distinct readout that is readily interpretable by pathologists. IHC, however, is a more expensive approach and not available at all medical centers. Virtually generating IHC images from H&E using deep neural networks thus becomes an attractive alternative. Deep generative models such as CycleGANs learn a semantically-consistent mapping between two image domai…
Advancing H&E-to-IHC Stain Translation in Breast Cancer: A Multi-Magnification and Attention-Based Approach
Fudan University（复旦大学）；College of Electronic Engineering, Ocean University of China（中国海洋大学电子工程学院）；Research Center for Frontier Fundamental Studies, Zhejiang Lab（之江实验室前沿基础研究中心）；Institutes of Biomedical Sciences, Fudan University（复旦大学生物医学研究院）
Towards Reliable Stain Transfer: An Iterative Data-Model Co-Optimization Framework Based on Multimodal Expert-Guided Assessment
East China Normal University（华东师范大学）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Hangzhou Hyperspectral Imaging Technology Co., Ltd.（杭州高光谱成像技术有限公司）；Fudan University Shanghai Cancer Center（复旦大学附属肿瘤医院）
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
In the clinic, resected tissue samples are stained with Hematoxylin-and-Eosin (H&E) and/or Immunhistochemistry (IHC) stains and presented to the pathologists on glass slides or as digital scans for diagnosis and assessment of disease progression. Cell-level quantification, e.g. in IHC protein expression scoring, can be extremely inefficient and subjective. We present DeepLIIF (https://deepliif.org), a first free online platform for efficient and reproducible IHC scoring. DeepLIIF outperforms current state-of-the-art approaches (relying on manual error-prone annotations) by virtually restaining…
While hematoxylin and eosin (H&E) is a standard staining procedure, immunohistochemistry (IHC) staining further serves as a diagnostic and prognostic method. However, acquiring special staining results requires substantial costs. Hence, we proposed a strategy for ultra-high-resolution unpaired image-to-image translation: Kernelized Instance Normalization (KIN), which preserves local information and successfully achieves seamless stain transformation with constant GPU memory usage. Given a patch, corresponding position, and a kernel, KIN computes local statistics using convolution operation. In…
Building Trust in Virtual Immunohistochemistry: Automated Assessment of Image Quality
Kahlert School of Computing, University of Utah（犹他大学卡勒特计算学院）；Department of Internal Medicine, Division of Epidemiology, University of Utah（犹他大学内科学系流行病学部门）；Department of Pathology, University of Utah（犹他大学病理学系）；ARUP Laboratories（ARUP实验室）
Leveraging Adversarial Learning for Pathological Fidelity in Virtual Staining
INESC TEC, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal（INESC TEC，工程学院， Porto 大学，Porto，葡萄牙）；The Netherlands Cancer Institute, Amsterdam, The Netherlands（荷兰癌症研究所，阿姆斯特丹，荷兰）；IMP Diagnostics, Porto, Portugal（IMP 诊断公司，Porto，葡萄牙）
STAR: A Fast and Robust Rigid Registration Framework for Serial Histopathological Images
School of Biological Science and Medical Engineering, Beihang University, Beijing, China（生物科学与医学工程学院，北航，北京，中国）；School of Computer Science and Engineering, Beihang University, Beijing, China（计算机科学与工程学院，北航，北京，中国）
StainNet: Scaling Self-Supervised Foundation Models on Immunohistochemistry and Special Stains for Computational Pathology
Tsinghua Shenzhen International Graduate School, Tsinghua University, China（清华大学深圳国际研究生院）；Biomedical Engineering Programme, City University of Hong Kong (Dongguan)（香港城市大学（东莞）生物医学工程项目）；Department of Pathology, Affiliated Hospital of Nantong University（南通大学附属医院病理科）；Nantong University（南通大学）
Rank-Aware Agglomeration of Foundation Models for Immunohistochemistry Image Cell Counting
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院）；School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；Department of Gastrointestinal Surgery and the Gastric Cancer Center, Zhongshan Hospital, Fudan University（复旦大学中山医院胃肠外科及胃癌中心）；School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；The Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University（西安交通大学第二附属医院综合乳腺护理中心）；Department of Pathology, The Second Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第二附属医院病理科）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Department of General Surgery, Zhongshan Hospital (Xiamen), Fudan University（复旦大学中山医院（厦门）外科科）
Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy
Aignostics, Germany（Aignostics，德国）；Institute of Pathology, Charité – Universitätsmedizin Berlin, Germany（柏林夏里特医学院病理学研究所）；Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Germany（柏林夏里特医学院柏林健康研究所）；Massachusetts General Hospital, Department of Pathology, Harvard Medical School, Boston, MA, US（哈佛医学院麻省总医院病理学系）；Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, US（梅奥诊所检验医学与病理学系）；Machine Learning Group, Technische Universität Berlin, Germany（柏林工业大学机器学习组）；BIFOLD – Berlin Institute for the Foundations of Learning and Data, Germany（柏林学习与数据基础研究所）；Department of Artificial Intelligence, Korea University, Republic of Korea（高丽大学人工智能系）；Max-Planck Institute for Informatics, Germany（马克斯·普朗克信息学研究所）；German Cancer Research Center (DKFZ) & German Cancer Consortium (DKTK), Berlin & Munich Partner Sites, Germany（德国癌症研究中心及德国癌症联盟柏林和慕尼黑合作站点）；Institute of Pathology, Ludwig-Maximilians-Universität München, Germany（慕尼黑大学病理学研究所）；Bavarian Cancer Research Center (BZKF), Germany（巴伐利亚癌症研究中心）
