Introduction to Whole Slide Image

Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images (WSIs).

Deep-learning methods for computational pathology require either manual annotation of gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and typically suffer from poor domain adaptation and interpretability.

Recently, deep learning methods for the analysis of whole-slide images (WSIs) have shown excellent performance on these tasks, and have the potential to substantially reduce the workload of pathologists.

An automated image analysis pipeline was developed to extract quantitative morphological features from H&E stained whole-slide images.

In conclusion, QuPath offers a user-friendly solution to whole-slide image analysis which could lead to important new discoveries in both health and disease.

We propose a pipeline, called TRIgrade, that will identify diagnostic relevant regions in the whole-slide image (WSI) and collectively predict the grade of the current WSI.

We developed and validated a deep learning-based system (HCC-SurvNet) that provides risk scores for disease recurrence after primary resection, directly from hematoxylin and eosin-stained digital whole-slide images of formalin-fixed, paraffin embedded liver resections.

Four CNNs that were pre-trained using transfer learning and one CNN built from scratch were used to classify patch images from pathology whole-slide images (WSIs).

In order to improve the generalization ability of the model, according to the features, predicting result could be obtained from random forest, and the integration result was invoked as the result of haematoxylin and eosin pathological whole-slide images (WSI).

Here, we developed a deep learning model using whole-slide images of regional lymph nodes of colorectal cancer with only a slide-level label (either a positive or negative slide).

Whole-slide images (WSIs) were stratified into training (n = 407), validation (n = 110), and test sets (n = 172).

We report a machine learning technique to quantify the extent of intestinal metaplasia and predict Barrett's segment length from whole-slide image tile counts automatically generated from Cytosponge-TFF3 histology slides.

We, therefore, developed a deep learning model for the fully automated CLR density quantification on routine hematoxylin and eosin (HE)-stained whole-slide images (WSIs) and further investigated its prognostic validity for patient stratification.

Nevertheless, the high-efficient and spatial-correlated processing of individual patches have always attracted attention in whole-slide image (WSI) analysis.

A convolutional neural network (CNN) method is proposed in this study to boost the automatic identification of breast cancer by analyzing hostile ductal carcinoma tissue zones in whole-slide images (WSIs).

1970 pCLE videos, 897,931 biopsy patches, and 387 whole-slide images were used to train, test, and validate the models.

We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO.

A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists.

e21012 Background: Pathologic classification of immune phenotype is challenging since there is no consensus on how to assess spatial relations of tumor-infiltrating lymphocyte (TIL) on cancer epithelium (CE) and cancer stroma (CS) in whole-slide images (WSI).

In this paper, we focused on the automatic evaluation of TME in giga-pixel digital histopathology whole-slide images.

We introduce UniToPatho, an annotated dataset of 9536 hematoxylin and eosin (H&E) stained patches extracted from 292 whole-slide images, meant for training deep neural networks for colorectal polyps classification and adenomas grading.

Methods: We have developed PodoSighter, an online cloud-based tool, to automatically identify and quantify podocyte nuclei from giga-pixel brightfield whole-slide images (WSIs) using deep learning.

Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field.

In recent years, the digitization of whole-slide images of tissue has accelerated the implementation of artificial intelligence (AI) approaches in tumor pathology and provided an opportunity to use AI tools to improve the interpretation of immune biomarkers.

In conclusion, QuPath offers a user-friendly, rapid and accurate solution to whole-slide image analysis which could lead to important new discoveries in both health and disease.

Deidentified urine cytology slides were digitalized as whole-slide images (WSIs) for analyzing.

In whole-slide image analysis in surgical specimens using immunohistochemistry, IgG4-related sclerosing sialadenitis (IgG4-SS, n = 17) was characterized by markedly numerous, large, and irregular-shaped GCs with increased IL-10 + cells and Tfr and Tfh in the total area of the salivary gland compared with controls, including patients with chronic sialadenitis (n = 17) and Sjögren syndrome (n = 15).

Advanced whole-slide image analysis algorithms, including convolutional neural networks (CNN) were used to register unmixed multispectral images and corresponding H&E sections, to segment the different tissue compartments (tumour, stroma) and to detect all individual positive lymphocytes.

After annotation of histopathological whole-slide images and image patch extraction, we trained and optimized an EfficientNet convolutional neuronal network algorithm on 84,139 image patches from 629 patients and evaluated its potential to classify tumor-free reference lymph nodes, nodal small lymphocytic lymphoma/chronic lymphocytic leukemia, and nodal diffuse large B-cell lymphoma.

The snapshot group included 1970 glomeruli from 516 patients, and the whole-slide image group included 8665 glomeruli from 148 patients.

METHODS: We selected 12 whole-slide images of radical prostatectomy specimens.

In this research, over the analysis of a privately collected and manually annotated dataset of 130 cytological whole-slide images, the authors proposed a deep-learning diagnostic system to localize, grade, and quantify squamous cell abnormalities.

Whole-slide images (WSIs) have supported the state-of-the-art diagnosis results and have been admitted as the gold standard clinically.

In this paper, a whole-slide image grading benchmark for cervical cancer precursor lesions is created and the "Uterine Cervical Cancer Database" introduced in this article is the first publicly available cervical tissue microscopy image dataset.

To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images.

82 for the whole-slide image.

Key Points Question Can a deep neural network decrease likelihood of unnecessary donor kidney discard by precisely quantifying percent global glomerulosclerosis on whole-slide images of hematoxylin-eosin–stained biopsy specimens? Findings In this prognostic study of 83 donor kidneys, a deep neural network segmented normal and globally sclerotic glomeruli in whole-slide images to quantify percent global glomerulosclerosis with higher performance than pathologists.

In this study, we demonstrate how deep learning approaches can be used for an automatic classification of glioma subtypes and grading using whole-slide images that were obtained from routine clinical practice.

In the testing phase, whole-slide images in an additional 40 cases were analysed.

Artificial intelligence provides a way to quantify mucus proportion on whole-slide images (WSIs) accurately.

The slides were multi spectrally imaged and custom-made python scripts enabled conversion to artificial brightfield whole-slide images (WSI).

Our experiments were performed on three publicly available whole-slide images of recent challenges (PAIP 2019: hepatocellular carcinoma segmentation; BACH 2020: breast cancer segmentation; CAMELYON 2016: metastasis detection in lymph nodes).

Using serial sectioning methodology combined with immunohistochemistry and whole-slide image analysis, the depth dependent variation in immune cell abundance in tumor specimens was investigated at single cell resolution.

In digital pathology, domain shift can be manifested in differences between whole-slide images, introduced by for example differences in acquisition pipeline – between medical centers or over time.

Hence, in this work we analyze how the advantages of deep learning can be used for the malignancy grading of histopathological whole-slide images of the prostate, without relying too much on annotations and large-scale datasets.

Color normalization is one of the pre-processing steps employed by many deep learning-based algorithms used for aiding pathology diagnoses with whole-slide images.

We developed a digital, automated quantification method to evaluate amyloid deposits in glomeruli, vessels and interstitium on digital whole-slide images (WSIs).

We developed methods for automated whole-slide image acquisition and unbiased computerized image analysis to quantify extravasated platelets.

The end-to-end deep learning framework thus automates digital pathology image workflow from tissue staining to interpretable prostate tumor classification and can be valuable for accurate grading of prostate cancer and generalized to other whole-slide image classification tasks.

In this paper, we develop such a method where we form an ensemble-based classification model using three Convolutional Neural Network (CNN) architectures, namely Inception v3, Xception and DenseNet-169 pre-trained on ImageNet dataset for Pap stained single cell and whole-slide image classification.

METHODS A total of 441 whole-slide images (WSIs) of AFS tissue material were used to develop a deep learning algorithm.

Here we adapt and apply the method to the histopathological setting by identifying a task as a whole-slide image with its corresponding classification problem.

To this end, we provide an overview on recent achievements and future prospects in deep learning methods applied to the analysis of radiology, endoscopy and histologic whole-slide images of the gastrointestinal tract.

However, detecting cancerous regions in whole-slide images has been challenging as it required substantial annotation and training efforts from clinicians and biologists.

Large volume of data generated by digitalized mammogram or whole-slide images can be interoperated through advanced machine learning.

In this work, we conducted a reproducibility study using the public datasets shared by the CAMELYON16 challenge, which aimed to develop and assess algorithms for the detection of breast cancer metastasis using whole-slide images (WSIs) of lymph node sections.

Methods: We collected a whole-slide image (WSI) of haematoxylin and eosin (H&E)-stained pathological slides from 592 HCC patients at the First Affiliated Hospital, College of Medicine, Zhejiang University between 2015 and 2020.

In digital pathology, different staining procedures and scanners cause substantial color variations in whole-slide images (WSIs), especially across different laboratories.

Computational pathology is burgeoning subspecialty in pathology that promises a better-integrated solution to whole-slide images, multi-omics data, and clinical informatics.

METHOD Once fibrosis had been established, liver samples were collected, histologically processed, stained with Masson's trichrome, and whole-slide images were captured with an appropriated digital pathology slide scanner.

Methods: Lunit SCOPE PD-L1 was developed by a total of 393,565 tumor cells annotated by board-certified pathologists for PD-L1 expression in 802 whole-slide images (WSI) stained by 22C3 pharmDx immunohistochemistry.

Background: Web-based digital slide viewers for pathology commonly use OpenSlide and OpenSeadragon (OSD) to access, visualize, and navigate whole-slide images (WSI).

In the present study, a fully automated approach was applied to distinguish differentiated/undifferentiated and non-mucinous/mucinous tumor types in GC tissue whole-slide images from The Cancer Genome Atlas (TCGA) stomach adenocarcinoma dataset (TCGA-STAD).

Whole-slide images (WSIs) are an important standard for the diagnosis of cervical cancer.

applied GANs to whole-slide images of p16-positive oropharyngeal squamous cell carcinoma (OPSCC) to automate the calculation of a multinucleation index (MuNI) for prognostication in p16-positive OPSCC.

Additionally, the spatially organized encoded feature map derived from small image patches is used to compress the gigapixel whole-slide images.

Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole-slide images (WSIs).

921 whole-slide images of lymph nodes were divided into two cohorts: training and testing.

We demonstrate that QuPath can be adequately used to analyze whole-slide images with the aim of identifying the islets of Langerhans and define their cellular composition as well as other basic morphological characteristics.

Here, we propose a deep-learning framework for analyzing lymph node whole-slide images (WSIs) to identify lymph nodes and tumor regions, and then to uncover tumor-area-to-MLN-area ratio (T/MLN).

This deep generative model is trained automatically by 27,775 patches of tumor vessels and cell nuclei decomposed from whole-slide images of 4 T1 breast cancer sections.

Here we present an automated, modular method for whole-slide image analysis of the spatial distribution of tumor-infiltrating CD8-positive lymphocytes.

Materials and Methods: Our retrospective validation included whole-slide images (WSIs) of 60 cases of histopathology and 20 cases each of frozen sections and a digital image-based breast algorithm after a washout period of 3 months.

METHODS A renal pathologist annotated renal biopsy specimens from 116 whole-slide images (WSIs) for IFTA and glomerulosclerosis.

The goal of the challenge was to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs).

71) on 712 whole-slide images.

Digital tissue image analysis has enabled users to extract quantitative and complex data from digitized whole-slide images.

6 million annotations from board-certified pathologists across >5700 samples to train deep learning models for cell and tissue classification that can exhaustively map whole-slide images at two and four micron-resolution.

We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO.

We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks.

We consider machine-learning-based thyroid-malignancy prediction from cytopathology whole-slide images (WSI).

Conclusions: Deep-learning models that analyze the TME from H&E whole-slide images can identify NSCLC patients with durable benefit on Pembrolizumab.

The goal of this study is to explore machine learning to predict the status of the three main CRC molecular pathways, microsatellite instability (MSI), chromosomal instability (CIN), CpG island methylator phenotype (CIMP), and to detect BRAF and TP53 mutations as well as to predict hypermutated (HM) CRC tumors from whole-slide images (WSIs) of colorectal cancer (CRC) slides stained with Hematoxylin and Eosin (H&E).

The slides were digitized into whole-slide images (WSIs), annotated to exclude the background and artefacts, and tessellated into square patches.

Existing nuclei segmentation methods have obtained limited results with multi-center and multi-organ whole-slide images (WSIs) due to the use of different stains, scanners, overlapping, clumped nuclei, and the ambiguous boundary between adjacent cell nuclei.

Meanwhile, accurate classification of image patches cropped from whole-slide images is essential for standard sliding window based histopathology slide classification methods.

The use of these CNN-based models offers users the ability to apply generalized lesion detection to whole-slide images, with the potential to generate novel quantitative data that would not be possible with conventional image analysis techniques.

In this paper, we apply multiplex immunofluorescence to MIBC tissue sections to capture whole-slide images and quantify potential prognostic markers related to lymphocytes, macrophages, tumour buds, and PD-L1.

Deep learning (DL) is able to automatically extract features from whole-slide images of prostate biopsies annotated by skilled pathologists and to classify the severity of PCa.

BackgroundThe possibility of digitizing whole-slide images (WSI) of tissue has led to the advent of artificial intelligence (AI) in digital pathology.

This study proposes a methodology which predicts selected gene expression values (microarray) from haematoxylin and eosin whole-slide images as an intermediate data modality to identify fulminant-like pulmonary tuberculosis ('supersusceptible') in an experimentally infected cohort of Diversity Outbred mice (n=77).

The performance of differentiating HGSOC versus SBOT achieved 91%–95% accuracy for 6485 imaging patches which have sufficient tumor and stroma cells (minimum of ten each) and 97% accuracy for classifying patients when aggregating the results to whole-slide image based on consensus.

Whole-slide images (WSI) are digitized representations of thin sections of stained tissue from various patient sources (biopsy, resection, exfoliation, fluid) and often exceed 100,000 pixels in any given spatial dimension.

One use of such technology is the analysis of whole-slide image tissue samples.