Single color digital H&E staining with In-and-Out Net
University of Texas at Austin（德克萨斯大学奥斯汀分校）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；The University of Texas MD Anderson Cancer Center（德克萨斯大学 MD 安德森癌症中心）
Mengkun Chen, Yen-Tung Liu, Fadeel Sher Khan, Matthew C. Fox, Jason S. Reichenberg, Fabiana C. P. S. Lopes, Katherine R. Sebastian, Mia K. Markey, James W. Tunnell
发表信息：
Computerized Medical Imaging and Graphics, volume = {118}, pages = {102468}, year = {2024}, issn = {0895-6111},
Virtual staining streamlines traditional staining procedures by digitally generating stained images from unstained or differently stained images. While conventional staining methods involve time-consuming chemical processes, virtual staining offers an efficient and low infrastructure alternative. Leveraging microscopy-based techniques, such as confocal microscopy, researchers can expedite tissue analysis without the need for physical sectioning. However, interpreting grayscale or pseudo-color microscopic images remains a challenge for pathologists and surgeons accustomed to traditional histologically stained images. To fill this gap, various studies explore digitally simulating staining to mimic targeted histological stains. This paper introduces a novel network, In-and-Out Net, specifically designed for virtual staining tasks. Based on Generative Adversarial Networks (GAN), our model efficiently transforms Reflectance Confocal Microscopy (RCM) images into Hematoxylin and Eosin (H&E) stained images. We enhance nuclei contrast in RCM images using aluminum chloride preprocessing for skin tissues. Training the model with virtual H\&E labels featuring two fluorescence channels eliminates the need for image registration and provides pixel-level ground truth. Our contributions include proposing an optimal training strategy, conducting a comparative analysis demonstrating state-of-the-art performance, validating the model through an ablation study, and collecting perfectly matched input and ground truth images without registration. In-and-Out Net showcases promising results, offering a valuable tool for virtual staining tasks and advancing the field of histological image analysis.
University of St Andrews（圣安德鲁斯大学）；Lothian NHS University Hospitals（洛锡安NHS大学医院）
The presence and density of specific types of immune cells are important to understand a patient's immune response to cancer. However, immunofluorescence staining required to identify T cell subtypes is expensive, time-consuming, and rarely performed in clinical settings. We present a framework to virtually stain Hoechst images (which are cheap and widespread) with both CD3 and CD8 to identify T cell subtypes in clear cell renal cell carcinoma using generative adversarial networks. Our proposed method jointly learns both staining tasks, incentivising the network to incorporate mutually benefic…
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 …
Virtual Staining of Label-Free Tissue in Imaging Mass Spectrometry
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；California NanoSystems Institute (CNSI)（加州纳米系统研究所）；Vanderbilt University（范德堡大学）；Delft University of Technology（代尔夫特理工大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Massachusetts Institute of Technology（麻省理工学院）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）
Histopathology tissue samples are widely available in two states: paraffin-embedded unstained and non-paraffin-embedded stained whole slide RGB images (WSRI). Hematoxylin and eosin stain (H&E) is one of the principal stains in histology but suffers from several shortcomings related to tissue preparation, staining protocols, slowness and human error. We report two novel approaches for training machine learning models for the computational H&E staining and destaining of prostate core biopsy RGB images. The staining model uses a conditional generative adversarial network that learns hierarchical …
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；California NanoSystems Institute（加州纳米系统研究所）；David Geffen School of Medicine（戴维·格芬医学院）
Histological analysis of tissue samples is one of the most widely used methods for disease diagnosis. After taking a sample from a patient, it goes through a lengthy and laborious preparation, which stains the tissue to visualize different histological features under a microscope. Here, we demonstrate a label-free approach to create a virtually-stained microscopic image using a single wide-field auto-fluorescence image of an unlabeled tissue sample, bypassing the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses a convolutional neura…
University of Strasbourg（斯特拉斯堡大学）；University of Belgrade（贝尔格莱德大学）；University of Kragujevac（克拉古耶瓦茨大学）；Hannover Medical School（汉诺威医学院）；University Clinic, Freiburg（弗赖堡大学诊所）
The application of supervised deep learning methods in digital pathology is limited due to their sensitivity to domain shift. Digital Pathology is an area prone to high variability due to many sources, including the common practice of evaluating several consecutive tissue sections stained with different staining protocols. Obtaining labels for each stain is very expensive and time consuming as it requires a high level of domain knowledge. In this article, we propose an unsupervised augmentation approach based on adversarial image-to-image translation, which facilitates the training of stain in…
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；California NanoSystems Institute (CNSI)（加州纳米系统研究所）；David Geffen School of Medicine（大卫·格芬医学院）
Using a deep neural network, we demonstrate a digital staining technique, which we term PhaseStain, to transform quantitative phase images (QPI) of labelfree tissue sections into images that are equivalent to brightfield microscopy images of the same samples that are histochemically stained. Through pairs of image data (QPI and the corresponding brightfield images, acquired after staining) we train a generative adversarial network (GAN) and demonstrate the effectiveness of this virtual staining approach using sections of human skin, kidney and liver tissue, matching the brightfield microscopy …
A Value Mapping Virtual Staining Framework for Large-scale Histological Imaging
School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）；National Engineering Research Center of Visual Technology, Peking University（北京大学视觉技术国家工程研究中心）；School of Computer Science, Peking University（北京大学计算机学院）；Medical School, Nanjing University（南京大学医学院）
The University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；The Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
Histopathological analysis is crucial in artery characterization for coronary artery disease (CAD). However, histology requires an invasive and time-consuming process. In this paper, we propose to generate virtual histology staining using Optical Coherence Tomography (OCT) images to enable real-time histological visualization. We develop a deep learning network, namely Coronary-GAN, to transfer coronary OCT images to virtual histology images. With a special consideration on the structural constraints in coronary OCT images, our method achieves better image generation performance than the conve…
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）
Nanyang Technological University（南洋理工大学）；Institute of Bioengineering and Bioimaging, A*STAR（A*STAR生物工程与生物成像研究所）
Staining is critical to cell imaging and medical diagnosis, which is expensive, time-consuming, labor-intensive, and causes irreversible changes to cell tissues. Recent advances in deep learning enabled digital staining via supervised model training. However, it is difficult to obtain large-scale stained/unstained cell image pairs in practice, which need to be perfectly aligned with the supervision. In this work, we propose a novel unsupervised deep learning framework for the digital staining of cell images using knowledge distillation and generative adversarial networks (GANs). A teacher mode…
A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；California NanoSystems Institute (CNSI)（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（南加利福尼亚大学）
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；California NanoSystems Institute (CNSI)（加州纳米系统研究所）；David Geffen School of Medicine（大卫·格芬医学院）
Histological staining is a vital step used to diagnose various diseases and has been used for more than a century to provide contrast to tissue sections, rendering the tissue constituents visible for microscopic analysis by medical experts. However, this process is time-consuming, labor-intensive, expensive and destructive to the specimen. Recently, the ability to virtually-stain unlabeled tissue sections, entirely avoiding the histochemical staining step, has been demonstrated using tissue-stain specific deep neural networks. Here, we present a new deep learning-based framework which generate…
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…
GANs vs. Diffusion Models for virtual staining with the HER2match dataset
The Netherlands Cancer Institute, Amsterdam, The Netherlands（荷兰癌症研究所）；Faculty of Engineering, University of Porto, Porto, Portugal（葡萄牙波尔图大学工程学院）；IMP Diagnostics, Porto, Portugal（IMP诊断（葡萄牙波尔图））
Label- and slide-free tissue histology using 3D epi-mode quantitative phase imaging and virtual H&E staining
University of California, Davis（加利福尼亚大学戴维斯分校）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Emory University School of Medicine（埃默里大学医学院）；UC Davis Health（加州大学戴维斯分校医疗系统）
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）
Digital Staining with Knowledge Distillation: A Unified Framework for Unpaired and Paired-But-Misaligned Data
Nanyang Technological University（南洋理工大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局材料研究与工程研究所）
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）
Generative Adversarial Networks (GANs) are state of the art for image synthesis. Here, we present dapi2ck, a novel GAN-based approach to synthesize cytokeratin (CK) staining from immunofluorescent (IF) DAPI staining of nuclei in non-small cell lung cancer (NSCLC) images. We use the synthetic CK to segment epithelial regions, which, compared to expert annotations, yield equally good results as segmentation on stained CK. Considering the limited number of markers in a multiplexed IF (mIF) panel, our approach allows to replace CK by another marker addressing the complexity of the tumor micro-envi…
Fluorescence microscopy plays a vital role in understanding the subcellular structures of living cells. However, it requires considerable effort in sample preparation related to chemical fixation, staining, cost, and time. To reduce those factors, we present a virtual fluorescence staining method based on deep neural networks (VirFluoNet) to transform fluorescence images of molecular labels into other molecular fluorescence labels in the same field-of-view. To achieve this goal, we develop and train a conditional generative adversarial network (cGAN) to perform digital fluorescence imaging dem…
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；California NanoSystems Institute (CNSI), University of California, Los Angeles（加利福尼亚大学洛杉矶分校加州纳米系统研究所）
Histological staining is the gold standard for tissue examination in clinical pathology and life-science research, which visualizes the tissue and cellular structures using chromatic dyes or fluorescence labels to aid the microscopic assessment of tissue. However, the current histological staining workflow requires tedious sample preparation steps, specialized laboratory infrastructure, and trained histotechnologists, making it expensive, time-consuming, and not accessible in resource-limited settings. Deep learning techniques created new opportunities to revolutionize staining methods by digi…
University of California, Los Angeles（加州大学洛杉矶分校）；California NanoSystems Institute, University of California, Los Angeles（加州大学洛杉矶分校加州纳米系统研究所）；David Geffen School of Medicine, University of California, Los Angeles（加州大学洛杉矶分校大卫·格芬医学院）
Deep learning-based virtual staining was developed to introduce image contrast to label-free tissue sections, digitally matching the histological staining, which is time-consuming, labor-intensive, and destructive to tissue. Standard virtual staining requires high autofocusing precision during the whole slide imaging of label-free tissue, which consumes a significant portion of the total imaging time and can lead to tissue photodamage. Here, we introduce a fast virtual staining framework that can stain defocused autofluorescence images of unlabeled tissue, achieving equivalent performance to v…
Multiplex immunofluorescence (MxIF) is an emerging imaging technique that produces the high sensitivity and specificity of single-cell mapping. With a tenet of 'seeing is believing', MxIF enables iterative staining and imaging extensive antibodies, which provides comprehensive biomarkers to segment and group different cells on a single tissue section. However, considerable depletion of the scarce tissue is inevitable from extensive rounds of staining and bleaching ('missing tissue'). Moreover, the immunofluorescence (IF) imaging can globally fail for particular rounds ('missing stain''). In th…
South China University of Technology（华南理工大学）；Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences（广东省人民医院（广东省医学科学院））；Southern Medical University（南方医科大学）
Color inconsistency is an inevitable challenge in computational pathology, which generally happens because of stain intensity variations or sections scanned by different scanners. It harms the pathological image analysis methods, especially the learning-based models. A series of approaches have been proposed for stain normalization. However, most of them are lack flexibility in practice. In this paper, we formulated stain normalization as a digital re-staining process and proposed a self-supervised learning model, which is called RestainNet. Our network is regarded as a digital restainer which…
CRUK Beatson Institute（CRUK比特森研究所）；Institute of Cancer Sciences（癌症科学研究所）；University of Glasgow（格拉斯哥大学）
Immunohistochemistry is a valuable diagnostic tool for cancer pathology. However, it requires specialist labs and equipment, is time-intensive, and is difficult to reproduce. Consequently, a long term aim is to provide a digital method of recreating physical immunohistochemical stains. Generative Adversarial Networks have become exceedingly advanced at mapping one image type to another and have shown promise at inferring immunostains from haematoxylin and eosin. However, they have a substantial weakness when used with pathology images as they can fabricate structures that are not present in th…
Label-free evaluation of lung and heart transplant biopsies using tissue autofluorescence-based virtual staining
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）；University of Southern California（南加州大学）；University of California, Davis（加州大学戴维斯分校）
Virtual histological staining of unlabeled autopsy tissue
University of California, Los Angeles（加州大学洛杉矶分校）；Xi’an Jiaotong University（西安交通大学）；University of California, San Francisco（加州大学旧金山分校）；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）
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）
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）
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）
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）
Generative Deep Learning for Computational Destaining and Restaining of Unregistered Digital Pathology Images
Department of Computer Science University of California Irvine, CA, USA（计算机科学系 加州大学 伊藤市 加州 USA）；Department of Pathology Stanford University Stanford, CA, USA（病理学系 斯坦福大学 斯坦福市 加州 USA）
We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from H&E stain to PHH3 and vice versa. We use the resulting synthetic images to build Convolutional Neural Networks (CNN) for automatic detection of mitotic figures, a strong prognostic biomarker used in routine breast cancer diagnosis and grading. We propose several scenarios, in which CNN trained with synthetically generated histopathology images perform on par with or even better than the same baseline model trained with real images. We discuss the potentia…
University of Strasbourg（斯特拉斯堡大学）；INSA-Lyon（法国国立应用科学学院里昂分校）；Université Claude Bernard Lyon 1（克洛德·贝尔纳里昂第一大学）；Hannover Medical School（汉诺威医学院）；Université de Haute Alsace（上阿尔萨斯大学）；University Clinic, Freiburg（弗莱堡大学医院）
Objective: This article presents an automatic image processing framework to extract quantitative high-level information describing the micro-environment of glomeruli in consecutive whole slide images (WSIs) processed with different staining modalities of patients with chronic kidney rejection after kidney transplantation. Methods: This four-step framework consists of: 1) approximate rigid registration, 2) cell and anatomical structure segmentation 3) fusion of information from different stainings using a newly developed registration algorithm 4) feature extraction. Results: Each step of the fr…
Virtual staining for 3D X-ray histology of bone implants
Institute of Materials Physics（材料物理研究所）；Helmholtz-Zentrum Hereon（海德堡研究中心）；Institute of Metallic Biomaterials（金属生物材料研究所）；Bruker Daltonics SPR（布鲁克尔达特利克斯SPR）；Data-Driven Analysis and Design of Materials（数据驱动的材料分析与设计）；University of Rostock（罗斯托克大学）
Université de Strasbourg（斯特拉斯堡大学）；Hannover Medical School（汉诺威医学院）；Université de Haute Alsace（上阿尔萨斯大学）
An important part of Digital Pathology is the analysis of multiple digitised whole slide images from differently stained tissue sections. It is common practice to mount consecutive sections containing corresponding microscopic structures on glass slides, and to stain them differently to highlight specific tissue components. These multiple staining modalities result in very different images but include a significant amount of consistent image information. Deep learning approaches have recently been proposed to analyse these images in order to automatically identify objects of interest for patho…
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）
Massachusetts Institute of Technology（麻省理工学院）；Harvard Medical School（哈佛医学院）；Boston University School of Medicine（波士顿大学医学院）；VA Boston Healthcare（波士顿退伍军人医疗中心）
Histopathological diagnoses of tumors in tissue biopsy after Hematoxylin and Eosin (H&E) staining is the gold standard for oncology care. H&E staining is slow and uses dyes, reagents and precious tissue samples that cannot be reused. Thousands of native nonstained RGB Whole Slide Image (RWSI) patches of prostate core tissue biopsies were registered with their H&E stained versions. Conditional Generative Adversarial Neural Networks (cGANs) that automate conversion of native nonstained RWSI to computational H&E stained images were then trained. High similarities between computational and H&E dye…
Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning
University of California, Los Angeles（加州大学洛杉矶分校）；California NanoSystems Institute (CNSI), University of California, Los Angeles（加州大学洛杉矶分校加州纳米系统研究所）；Fatih Sultan Mehmet Vakif University（法提赫·苏丹·穆罕默德·瓦克夫大学）；David Geffen School of Medicine at the University of California, Los Angeles（加州大学洛杉矶分校大卫·格芬医学院）；Hadassah Hebrew University Medical Center（哈达萨希伯来大学医学中心）；Keck School of Medicine, University of Southern California（南加州大学凯克医学院）
University of California, Los Angeles（加州大学洛杉矶分校）；California NanoSystems Institute (CNSI)（加州纳米系统研究所）；David Geffen School of Medicine（戴维·格芬医学院）
Pathological diagnosis relies on the visual inspection of histologically stained thin tissue specimens, where different types of stains are applied to bring contrast to and highlight various desired histological features. However, the destructive histochemical staining procedures are usually irreversible, making it very difficult to obtain multiple stains on the same tissue section. Here, we demonstrate a virtual stain transfer framework via a cascaded deep neural network (C-DNN) to digitally transform hematoxylin and eosin (H&E) stained tissue images into other types of histological stains. U…
École de Technologie Supérieure, Université du Québec（魁北克大学高等技术学院）
Breast cancer is a health problem that affects mainly the female population. An early detection increases the chances of effective treatment, improving the prognosis of the disease. In this regard, computational tools have been proposed to assist the specialist in interpreting the breast digital image exam, providing features for detecting and diagnosing tumors and cancerous cells. Nonetheless, detecting tumors with a high sensitivity rate and reducing the false positives rate is still challenging. Texture descriptors have been quite popular in medical image analysis, particularly in histopath…
Normalizing unwanted color variations due to differences in staining processes and scanner responses has been shown to aid machine learning in computational pathology. Of the several popular techniques for color normalization, structure preserving color normalization (SPCN) is well-motivated, convincingly tested, and published with its code base. However, SPCN makes occasional errors in color basis estimation leading to artifacts such as swapping the color basis vectors between stains or giving a colored tinge to the background with no tissue. We made several algorithmic improvements to remove…
University of Toronto（多伦多大学）；University of British Columbia（不列颠哥伦比亚大学）；Sunnybrook Research Institute（森尼布鲁克研究所）
Unsupervised learning has been a long-standing goal of machine learning and is especially important for medical image analysis, where the learning can compensate for the scarcity of labeled datasets. A promising subclass of unsupervised learning is self-supervised learning, which aims to learn salient features using the raw input as the learning signal. In this paper, we use a contrastive self-supervised learning method called SimCLR that achieved state-of-the-art results on natural-scene images and apply this method to digital histopathology by collecting and pretraining on 57 histopathology …
University of Waterloo（滑铁卢大学）；Ontario Tech University（安大略理工大学）；Mayo Clinic（梅奥诊所）
Whole Slide Images (WSIs) in digital pathology are used to diagnose cancer subtypes. The difference in procedures to acquire WSIs at various trial sites gives rise to variability in the histopathology images, thus making consistent diagnosis challenging. These differences may stem from variability in image acquisition through multi-vendor scanners, variable acquisition parameters, and differences in staining procedure; as well, patient demographics may bias the glass slide batches before image acquisition. These variabilities are assumed to cause a domain shift in the images of different hospi…
