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）
A unified multiplex virtual staining model enables scalable and non-destructive multiplex analysis from H&E slides while promoting parameter efficiency, shared pathological knowledge, and consistent cross-biomarker representations. However, in clinical practice, data for new biomarkers are typically acquired sequentially over time. Fine-tuning on such temporally arriving data leads to severe performance degradation on previously learned biomarkers, as sequential optimization disrupts the structured relationships among biomarker representations in the latent space. To address this issue, we propose ContiStain, an IHC multi-domain relational distillation framework for continual virtual staining. We first (i) construct a domain-aware structured feature space using a mixture-of-experts (MoE) feature extractor to reduce representation interference across biomarker domains. Based on this stabilized feature space, we then (ii) propose a relation-preserving distillation strategy that explicitly enforces the consistency of cross-domain token-level cosine similarity matrices between learned biomarker domains during continual adaptation. By maintaining cross-domain structural coherence, ContiStain mitigates forgetting while retaining adaptability to new domains. Experiments on the MIST dataset under a four-domain sequential virtual IHC staining setting show improved stability, reducing FID and ConchFID by 11.1 and 60.9 compared to sequential fine-tuning, enabling scalable and robust multi-domain virtual staining. Code is released at https://github.com/ccitachi/ContiStain.
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）
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）
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）
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）
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）
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）
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）
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）
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）
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
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…
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 …
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）
Westlake University（西湖大学）；Westlake Institute for Advanced Study（西湖高等研究院）；Zhejiang University（浙江大学）
Ki67 is a significant biomarker in the diagnosis and prognosis of cancer, whose index can be evaluated by quantifying its expression in Ki67 immunohistochemistry (IHC) stained images. However, quantitative analysis on multi-source Ki67 images is yet a challenging task in practice due to cross-domain distribution differences, which result from imaging variation, staining styles, and lesion types. Many recent studies have made some efforts on domain generalization (DG), whereas there are still some noteworthy limitations. Specifically in the case of Ki67 images, learning invariant representation…
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）
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）
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）
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
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）
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）
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 …
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）
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）
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）
SIMPLER: H&E-Informed Representation Learning for Structured Illumination Microscopy
Kahlert School of Computing, University of Utah, Salt Lake City, UT 84112, USA Scientific Computing \& Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA Instapath Inc., Houston, TX 77021 Tulane University, Department of Biomedical Engineering, New Orleans, Louisiana, United States
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）
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）
