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arXiv 2311.06552eess.IVcs.CVcs.LG
染色一致性学习：处理数字病理分割中的染色变异
Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation
Imperial College London（伦敦帝国理工学院）；The Babraham Institute（巴布拉汉研究所）；University of Cambridge（剑桥大学）；National Heart & Lung Institute, Imperial College London（伦敦帝国理工学院国家心肺研究所）；Royal Brompton Hospital（皇家布朗普顿医院）
Michael Yeung, Todd Watts, Sean YW Tan, Pedro F. Ferreira, Andrew D. Scott, Sonia Nielles-Vallespin, Guang Yang
Stain variation is a unique challenge associated with automated analysis of digital pathology. Numerous methods have been developed to improve the robustness of machine learning methods to stain variation, but comparative studies have demonstrated limited benefits to performance. Moreover, methods to handle stain variation were largely developed for H&E stained data, with evaluation generally limited to classification tasks. Here we propose Stain Consistency Learning, a novel framework combining stain-specific augmentation with a stain consistency loss function to learn stain colour invariant features. We perform the first, extensive comparison of methods to handle stain variation for segmentation tasks, comparing ten methods on Masson's trichrome and H&E stained cell and nuclei datasets, respectively. We observed that stain normalisation methods resulted in equivalent or worse performance, while stain augmentation or stain adversarial methods demonstrated improved performance, with the best performance consistently achieved by our proposed approach. The code is available at: https://github.com/mlyg/stain_consistency_learning
In pathology, tissue samples are assessed using multiple staining techniques to enhance contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN based graph fusion approach that leverages complementary information from multiple non-registered histopathology images to predict pathologic scores. We demonstrate this approach in nonalcoholic steatohepatitis (NASH) by predicting CRN fibrosis stage and NAFLD Activity Score (NAS). Primary assessment of NASH typically requires liver biopsy evaluation on two histological stains: Trichrome (TC) and hematoxylin and eosin (…
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）
Radboud University Medical Center（拉德堡德大学医学中心）；Linköping University（林雪平大学）
Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift between laboratories, convolutional neural networks (CNNs) trained with images from one lab often underperform on unseen images from the other lab. Several techniques have been proposed to reduce the generalization error, mainly grouped into two categories: stain color augmentation and stain color normalization. The former simulates a wide variety of realistic stain variations during training, producing stain-invarian…
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…
Unsupervised and unpaired domain translation using generative adversarial neural networks, and more precisely CycleGAN, is state of the art for the stain translation of histopathology images. It often, however, suffers from the presence of cycle-consistent but non structure-preserving errors. We propose an alternative approach to the set of methods which, relying on segmentation consistency, enable the preservation of pathology structures. Focusing on immunohistochemistry (IHC) and multiplexed immunofluorescence (mIF), we introduce a simple yet effective guidance scheme as a loss function that…
Institute of Network Technology（网络技术研究所）；School of Automation（自动化学院）；Beijing University of Posts and Telecommunications（北京邮电大学）
Automatic cell segmentation is an essential step in the pipeline of computer-aided diagnosis (CAD), such as the detection and grading of breast cancer. Accurate segmentation of cells can not only assist the pathologists to make a more precise diagnosis, but also save much time and labor. However, this task suffers from stain variation, cell inhomogeneous intensities, background clutters and cells from different tissues. To address these issues, we propose an Attention Enforced Network (AENet), which is built on spatial attention module and channel attention module, to integrate local features …
University of Strasbourg（斯特拉斯堡大学）；CNRS (UMR 7357)（法国国家科学研究中心（UMR 7357））；University of Kragujevac（克拉古耶瓦茨大学）；Hannover Medical School（汉诺威医学院）
Virtual stain transfer is a promising area of research in Computational Pathology, which has a great potential to alleviate important limitations when applying deeplearningbased solutions such as lack of annotations and sensitivity to a domain shift. However, in the literature, the majority of virtual staining approaches are trained for a specific staining or stain combination, and their extension to unseen stainings requires the acquisition of additional data and training. In this paper, we propose HistoStarGAN, a unified framework that performs stain transfer between multiple stainings, stai…
Institute of Information Systems, HES-SO (University of Applied Sciences and Arts Western Switzerland)（瑞士西部应用科学与艺术大学 信息系统研究所）；Department of Computer Science, University of Geneva（日内瓦大学 计算机科学系）；Department of Neurosciences, University of Padua（帕多瓦大学 神经科学系）；Inselspital, Bern Hospital（伯尔尼大学医院）；Medical Faculty, University of Geneva（日内瓦大学 医学院）
Computational pathology is a domain that aims to develop algorithms to automatically analyze large digitized histopathology images, called whole slide images (WSI). WSIs are produced scanning thin tissue samples that are stained to make specific structures visible. They show stain colour heterogeneity due to different preparation and scanning settings applied across medical centers. Stain colour heterogeneity is a problem to train convolutional neural networks (CNN), the state-of-the-art algorithms for most computational pathology tasks, since CNNs usually underperform when tested on images in…
ParamNet: A Dynamic Parameter Network for Fast Multi-to-One Stain Normalization
Southern Medical University（南方医科大学）；Huazhong University of Science and Technology（华中科技大学）；Tongji Hospital（同济医院）；Hubei Maternal and Child Health Hospital（湖北省妇幼保健院）
University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）；Huron Digital Pathology（休伦数字病理公司）
Out-of-focus microscopy lens in digital pathology is a critical bottleneck in high-throughput Whole Slide Image (WSI) scanning platforms, for which pixel-level automated Focus Quality Assessment (FQA) methods are highly desirable to help significantly accelerate the clinical workflows. Existing FQA methods include both knowledge-driven and data-driven approaches. While data-driven approaches such as Convolutional Neural Network (CNN) based methods have shown great promises, they are difficult to use in practice due to their high computational complexity and lack of transferability. Here, we pr…
Histopathology images; microscopy images of stained tissue biopsies contain fundamental prognostic information that forms the foundation of pathological analysis and diagnostic medicine. However, diagnostics from histopathology images generally rely on a visual cognitive assessment of tissue slides which implies an inherent element of interpretation and hence subjectivity. Access to digitized histopathology images enabled the development of computational systems aiming at reducing manual intervention and automating parts of pathologists' workflow. Specifically, applications of deep learning to…
Mars Digital Technologies（玛氏数字科技）；Antech Diagnostics（Antech诊断公司）；Stanford University（斯坦福大学）
Background: Histopathology is an important modality for the diagnosis and management of many diseases in modern healthcare, and plays a critical role in cancer care. Pathology samples can be large and require multi-site sampling, leading to upwards of 20 slides for a single tumor, and the human-expert tasks of site selection and and quantitative assessment of mitotic figures are time consuming and subjective. Automating these tasks in the setting of a digital pathology service presents significant opportunities to improve workflow efficiency and augment human experts in practice. Approach: Mul…
Unlocking adaptive digital pathology through dynamic feature learning
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；University of Oxford（牛津大学）；Fourth Military Medical University（第四军医大学）；Third Military Medical University（第三军医大学）；Chongqing Advanced Pathology Research Institute（重庆先进病理研究院）；Jinfeng Laboratory（金凤实验室）；Soochow University（苏州大学）；The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital（郑州大学附属肿瘤医院暨河南省肿瘤医院）；Research Institute of Tsinghua, Pearl River Delta（清华大学珠三角研究院）
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…
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）
Tencent AI Lab（腾讯AI实验室）；University of Connecticut（康涅狄格大学）；The Fourth Hospital of Hebei Medical University（河北医科大学第四医院）；West China Hospital, Sichuan University（四川大学华西医院）
Artificial Intelligence (AI)-powered pathology is a revolutionary step in the world of digital pathology and shows great promise to increase both diagnosis accuracy and efficiency. However, defocus and motion blur can obscure tissue or cell characteristics hence compromising AI algorithms'accuracy and robustness in analyzing the images. In this paper, we demonstrate a deep-learning-based approach that can alleviate the defocus and motion blur of a microscopic image and output a sharper and cleaner image with retrieved fine details without prior knowledge of the blur type, blur extent and patho…
Adaptive Stain Normalization for Cross-Domain Medical Histology
Dept. of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA（生物医学工程系，约翰霍普金斯大学，巴尔的摩，MD，USA）；Johns Hopkins Malaria Research Institute, W. Harry Feinstone Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Baltimore, MD, USA（约翰霍普金斯疟疾研究 institute，W. Harry Feinstone 分子微生物学与免疫学系，布隆伯格公共卫生学院，巴尔的摩，MD，USA）；Dept. of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA（医学系，约翰霍普金斯大学医学院，巴尔的摩，MD，USA）；Dept. of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA（电气与系统工程系，宾夕法尼亚大学，费城，PA，USA）
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 …
Integrating Pathology Foundation Models and Spatial Transcriptomics for Cellular Decomposition from Histology Images
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA（电气与计算机工程学院，佐治亚理工学院）；Department of Biomedical Engineering, Georgia Institute of Technology（生物医学工程系，佐治亚理工学院）；Emory University, Atlanta, GA, USA（埃默里大学）
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…
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…
Technical University of Munich（慕尼黑技术大学）；TUM School of Medicine and Health（慕尼黑技术大学医学院与健康学院）；TUM School of Computation, Information and Technology（慕尼黑技术大学计算、信息与技术学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Munich Data Science Institute（慕尼黑数据科学研究所）
University of Warwick（华威大学）；GlaxoSmithKline（葛兰素史克）
"Is it possible to predict expression levels of different genes at a given spatial location in the routine histology image of a tumor section by modeling its stain absorption characteristics?" In this work, we propose a "stain-aware" machine learning approach for prediction of spatial transcriptomic gene expression profiles using digital pathology image of a routine Hematoxylin & Eosin (H&E) histology section. Unlike recent deep learning methods which are used for gene expression prediction, our proposed approach termed Neural Stain Learning (NSL) explicitly models the association of stain abs…
National University of Singapore（新加坡国立大学）；Duke-NUS, Graduate Medical School（杜克-新加坡国立大学医学院）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡全国眼科中心）；Université de la Polynésie francaise（法属波利尼西亚大学）；Moorfields Eye Hospital NHS Foundation Trust（Moorfields眼科医院NHS基金会信托）；UCL Institute of Ophthalmol（伦敦大学学院眼科研究所）
Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneous isolation from optical coherence tomography (OCT) images may be of great interest for the clinical diagnosis and management of this pathology. A deep learning algorithm was designed and trained to digitally stain (i.e. highlight) 6 ONH tissue layers by capturing both the local (tissue texture) and contextual information (spatial arrangement of tissues). The overall dice coefficient (mean of all tissues) was $0.91 \…
St. Olavs hospital, Trondheim University Hospital（圣奥拉夫医院，特隆赫姆大学医院）；NTNU - Norwegian University of Science and Technology（挪威科技大学）；University of Helsinki（赫尔辛基大学）；SINTEF Digital
Application of deep learning on histopathological whole slide images (WSIs) holds promise of improving diagnostic efficiency and reproducibility but is largely dependent on the ability to write computer code or purchase commercial solutions. We present a code-free pipeline utilizing free-to-use, open-source software (QuPath, DeepMIB, and FastPathology) for creating and deploying deep learning-based segmentation models for computational pathology. We demonstrate the pipeline on a use case of separating epithelium from stroma in colonic mucosa. A dataset of 251 annotated WSIs, comprising 140 hem…
Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham（阿姆里塔·维什瓦·维迪亚皮塔姆大学无线网络与应用中心）；Valeo（法雷奥）；Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham（阿姆里塔·维什瓦·维迪亚皮塔姆大学计算机科学与工程系）；School of Computing and Electrical Engineering, IIT Mandi（印度理工学院曼迪分校计算与电气工程学院）
Computer assisted diagnosis in digital pathology is becoming ubiquitous as it can provide more efficient and objective healthcare diagnostics. Recent advances have shown that the convolutional Neural Network (CNN) architectures, a well-established deep learning paradigm, can be used to design a Computer Aided Diagnostic (CAD) System for breast cancer detection. However, the challenges due to stain variability and the effect of stain normalization with such deep learning frameworks are yet to be well explored. Moreover, performance analysis with arguably more efficient network models, which may…
RWTH Aachen University（亚琛工业大学）；Hannover Medical School（汉诺威医学院）；Oncotest GmbH（Oncotest有限公司）
While human observers are able to cope with variations in color and appearance of histological stains, digital pathology algorithms commonly require a well-normalized setting to achieve peak performance, especially when a limited amount of labeled data is available. This work provides a fully automated, end-to-end learning-based setup for normalizing histological stains, which considers the texture context of the tissue. We introduce Feature Aware Normalization, which extends the framework of batch normalization in combination with gating elements from Long Short-Term Memory units for normaliz…
Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology
Department of Computer Science Vanderbilt University, TN, USA（计算机科学系范德堡大学）；Department of Radiology Weill Cornell Medicine, NY, USA（放射学系韦尔·科恩医学中心）；Department of Medicine Vanderbilt University Medical Center, TN, USA Veterans Affairs Tennessee Valley Healthcare System TN, USA（医学系范德堡大学医学中心退伍军人事务田纳西山谷医疗系统）；Department of Medicine Vanderbilt University Medical Center Veterans Affairs Tennessee Valley Healthcare System TN, USA（医学系范德堡大学医学中心退伍军人事务田纳西山谷医疗系统）；Department of Anatomical Pathology UT MD Anderson Cancer Center,TX, USA（解剖病理学系UT MD安德森癌症中心）
University of Utah（犹他大学）；Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；Huntsman Cancer Institute, University of Utah（犹他大学亨茨曼癌症研究所）
We propose a novel semi-supervised learning approach for classification of histopathology images. We employ strong supervision with patch-level annotations combined with a novel co-training loss to create a semi-supervised learning framework. Co-training relies on multiple conditionally independent and sufficient views of the data. We separate the hematoxylin and eosin channels in pathology images using color deconvolution to create two views of each slide that can partially fulfill these requirements. Two separate CNNs are used to embed the two views into a joint feature space. We use a contr…
A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images
Department of Electrical and Computer Engineering, University of Florida（佛罗里达大学电气与计算机工程系）；Seed Production Innovation, Crop Science Division, Bayer Company（拜耳公司种子生产创新部）；Division of Medicine – Quantitative Health, University of Florida（佛罗里达大学医学部-定量健康分部）；Department of Health Outcomes and Biomedical Informatics, University of Florida（佛罗里达大学健康结果与生物医学信息学系）；Department of Pathology, Immunology and Laboratory Medicine, University of Florida College of Medicine（佛罗里达大学医学院病理学、免疫学与实验室医学系）；Kidney Disease Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health（美国国立卫生研究院糖尿病、消化系统与肾病研究所肾病分支）；Indiana University School of Medicine（印第安纳大学医学院）；Departments of Medicine, Washington University School of Medicine（华盛顿大学医学院医学部）；Universidade de Coimbra（科英布拉大学）；Department of Pathology and Laboratory Medicine, University of California at Davis School of Medicine（加州大学戴维斯分校医学院病理学与实验室医学系）；Department of Pathology, Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院病理学系）
TU Darmstadt（达姆施塔特工业大学）；University Hospital Cologne（科隆大学医院）
Although deep federated learning has received much attention in recent years, progress has been made mainly in the context of natural images and barely for computational pathology. However, deep federated learning is an opportunity to create datasets that reflect the data diversity of many laboratories. Further, the effort of dataset construction can be divided among many. Unfortunately, existing algorithms cannot be easily applied to computational pathology since previous work presupposes that data distributions of laboratories must be similar. This is an unlikely assumption, mainly since dif…
Virchow: A Million-Slide Digital Pathology Foundation Model
Paige；Microsoft Research（微软研究院）；NSW Health Pathology（新南威尔士州卫生病理学）；St George Hospital（圣乔治医院）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；University of Rochester（罗切斯特大学）
Systematic Evaluation of Preprocessing Techniques for Accurate Image Registration in Digital Pathology
Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena（耶拿弗里德里希·席勒大学物理化学研究所与阿贝光子学中心）；Leibniz Institute of Photonic Technology（莱布尼茨光子技术研究所）
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 …
University of Science and Technology of China（中国科学技术大学）；UNSW Sydney（新南威尔士大学）；Genmab Inc.（Genmab公司）
NLP-based computer vision models, particularly vision transformers, have been shown to outperform CNN models in many imaging tasks. However, most digital pathology artificial-intelligence models are based on CNN architectures, probably owing to a lack of data regarding NLP models for pathology images. In this study, we developed digital pathology pipelines to benchmark the five most recently proposed NLP models (vision transformer (ViT), Swin Transformer, MobileViT, CMT, and Sequencer2D) and four popular CNN models (ResNet18, ResNet50, MobileNetV2, and EfficientNet) to predict biomarkers in co…
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）
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；University of Ulsan College of Medicine（蔚山大学医学院）；Asan Medical Center（峨山医学中心）；Ajou University School of Medicine（亚洲大学医学院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
Deep learning has been widely used to analyze digitized hematoxylin and eosin (H&E)-stained histopathology whole slide images. Automated cancer segmentation using deep learning can be used to diagnose malignancy and to find novel morphological patterns to predict molecular subtypes. To train pixel-wise cancer segmentation models, manual annotation from pathologists is generally a bottleneck due to its time-consuming nature. In this paper, we propose Deep Interactive Learning with a pretrained segmentation model from a different cancer type to reduce manual annotation time. Instead of annotatin…
Generative Adversarial Networks for Stain Normalisation in Histopathology
University of Leeds（利兹大学）；Leeds Cancer Centre（利兹癌症中心）；St James’s University Hospital（圣詹姆斯大学医院）；Leeds Institute of Medical Research at St James’s（圣詹姆斯利兹医学研究所）；School of Medicine（医学院）；School of Computing（计算机学院）；Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)（生物医学计算成像与模拟技术中心（CISTIB））
Semantic Mosaicing of Histo-Pathology Image Fragments using Visual Foundation Models
Computational Imaging Research Lab（计算成像研究实验室）；Division of General and Pediatric Radiology（普通放射科与小儿放射科 division）；Christian Doppler Laboratory for Machine Learning Driven Precision Imaging（Christian Doppler 机器学习驱动的精准成像实验室）；Comprehensive Center for Artificial Intelligence in Medicine（医学人工智能综合中心）；Department of Pathology（病理学系）
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…
University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
Recent studies have shown promising results in using Deep Learning to detect malignancy in whole slide imaging. However, they were limited to just predicting positive or negative finding for a specific neoplasm. We attempted to use Deep Learning with a convolutional neural network algorithm to build a lymphoma diagnostic model for four diagnostic categories: benign lymph node, diffuse large B cell lymphoma, Burkitt lymphoma, and small lymphocytic lymphoma. Our software was written in Python language. We obtained digital whole slide images of Hematoxylin and Eosin stained slides of 128 cases in…
