Whole Slide Imaging - Science method

The current subjective histopathological assessment of cutaneous melanoma is challenging. The application of image analysis algorithms to histological images may facilitate improvements in workflow and prognostication. To date, several individual algorithms applied to melanoma histological images have been reported with variations in approach and r...

Multiple instance learning (MIL) has emerged as a popular method for classifying histopathology whole slide images (WSIs). However, existing approaches typically rely on pre-trained models from large natural image datasets, such as ImageNet, to generate instance features, which can be sub-optimal due to the significant differences between natural i...

Deep learning methods are widely applied in digital pathology to address clinical challenges such as prognosis and diagnosis. As one of the most recent applications, deep models have also been used to extract molecular features from whole slide images. Although molecular tests carry rich information, they are often expensive, time-consuming, and re...

Although the histopathological diagnosis of cutaneous melanocytic lesions is fairly accurate and reliable among experienced surgical pathologists, it is not perfect in every case (especially melanoma). Microscopic examination–clinicopathological correlation is the gold standard for the definitive diagnosis of melanoma. Pathologists may encounter di...

According to GLOBOCAN 2020, prostate cancer is the second most common cancer in men worldwide and the fourth most prevalent cancer overall. For pathologists, grading prostate cancer is challenging, especially when discriminating between Grade 3 (G3) and Grade 4 (G4). This paper proposes a Self-Supervised Learning (SSL) framework to classify prostat...

Supervised deep learning methods have achieved considerable success in medical image analysis, owing to the availability of large-scale and well-annotated datasets. However, creating such datasets for whole slide images (WSIs) in histopathology is a challenging task due to their gigapixel size. In recent years, self-supervised learning (SSL) has em...

The rapid development of histopathology scanners allowed the digital transformation of pathology. Current devices fastly and accurately digitize histology slides on many magnifications, resulting in whole slide images (WSI). However, direct application of supervised deep learning methods to WSI highest magnification is impossible due to hardware li...

p>Early detection of breast cancer through mammography is crucial for successful treatment. Microcalcifications are small deposits of calcium in breast ducts, they can be an indication of breast cancer and are first detected by mammography. However, their presence must be confirmed by an histopathologist through slides examination. As a help to pra...

Background:Breast cancer research has come a long way since its inception, with scientists and researchers continuously delving deeper. The focus has broadened from studying only tumor cells to now including a more holistic approach, which encompasses the study of tumor-associated stromata and infiltrating lymphocytes (TILs). Furthermore, the negat...

Northern Europe has the second highest mortality rate of melanoma globally. In 2020, the mortality rate of melanoma rose to 1.9 per 100 000 habitants. Melanoma prognosis is based on a pathologist's subjective visual analysis of the patient's tumor. This methodology is heavily time-consuming, and the prognosis variability among experts is notable, d...

Transferring prior knowledge from a source domain to the same or similar target domain can greatly enhance the performance of models on the target domain. However, it is challenging to directly leverage the knowledge from the source domain due to task discrepancy and domain shift. To bridge the gaps between different tasks and domains, we propose a...

Cervical cancer (CC) is the fourth most common malignant tumor among women worldwide. Constructing a high-accuracy deep convolutional neural network (DCNN) for cervical cancer screening and diagnosis is important for the successful prevention of cervical cancer. In this work, we proposed a robust DCNN for cervical cancer screening using whole-slide...

Grading precancerous lesions on whole slide images is a challenging task: the continuous space of morphological phenotypes makes clear-cut decisions between different grades often difficult, leading to low inter- and intra-rater agreements. More and more Artificial Intelligence (AI) algorithms are developed to help pathologists perform and standard...

Purpose Endometrial histology on hematoxylin and eosin (H&E)–stained preparations provides information associated with receptivity. However, traditional histological examination by Noyes’ dating method is of limited value as it is prone to subjectivity and is not well correlated with fertility status or pregnancy outcome. This study aims to mitigat...

Motivation: Multiple instance learning (MIL) is a powerful technique to classify whole slide images (WSIs) for diagnostic pathology. The key challenge of MIL on WSI classification is to discover the critical instances that trigger the bag label. However, tumor heterogeneity significantly hinders the algorithm’s performance. Results: Here, we propos...

Traditionally, pathological analysis and diagnosis are performed by manually eyeballing glass-slide specimen under a microscope by an expert. Whole slide image (WSI) is the digital specimen produced from the glass-slide. WSI enabled specimen to be observed on a computer-screen and led to computational pathology where computer-vision and artificial...

Imaging mass cytometry (IMC) is a powerful multiplexed tissue imaging technology that allows simultaneous detection of more than 30 makers on a single slide. It has been increasingly used for singlecell-based spatial phenotyping in a wide range of samples. However, it only acquires a small, rectangle field of view (FOV) with a low image resolution...

Diabetic nephropathy (DN) in the context of type 2 diabetes is the leading cause of end-stage renal disease (ESRD) in the United States. DN is graded based on glomerular morphology and has a spatially heterogeneous presentation in kidney biopsies that complicates pathologists predictions of disease progression. Artificial intelligence and deep lear...

Weakly-supervised classification of histopathology slides is a computationally intensive task, with a typical whole slide image (WSI) containing billions of pixels to process. We propose Discriminative Region Active Sampling for Multiple Instance Learning (DRAS-MIL), a computationally efficient slide classification method using attention scores to...

Purpose Classification and grading of central nervous system (CNS) tumours play a critical role in the clinic. When WHO CNS5 simplifies the histopathology diagnosis and places greater emphasis on molecular pathology, artificial intelligence (AI) has been widely used to meet the increased need for an automatic histopathology scheme that could libera...

Melanoma diagnosed and treated in its early stages can increase the survival rate. A projected increase in skin cancer incidents and a dearth of dermatopathologists have emphasized the need for computational pathology (CPATH) systems. CPATH systems with deep learning (DL) models have the potential to identify the presence of melanoma by exploiting...

p>Early detection of breast cancer through mammography is crucial for successful treatment. Microcalcifications are small deposits of calcium in breast ducts, they can be an indication of breast cancer and are first detected by mammography. However, their presence must be confirmed by an histopathologist through slides examination. As a help to pra...

p>Early detection of breast cancer through mammography is crucial for successful treatment. Microcalcifications are small deposits of calcium in breast ducts, they can be an indication of breast cancer and are first detected by mammography. However, their presence must be confirmed by an histopathologist through slides examination. As a help to pra...

In pre-clinical pathology, there is a paradox between the abundance of raw data (whole slide images from many organs of many individual animals) and the lack of pixel-level slide annotations done by pathologists. Due to time constraints and requirements from regulatory authorities, diagnoses are instead stored as slide labels. Weakly supervised tra...

Although the tumor-stroma ratio (TSR) has prognostic value in many cancers, the traditional semi-quantitative visual assessment method has inter-observer variability, making it impossible for clinical practice. We aimed to develop a machine learning (ML) algorithm for accurately quantifying TSR in hematoxylin-and-eosin (H&E)-stained whole slide ima...

Purpose: This study aimed to find suitable source domain data in cross-domain transfer learning to extract robust image features. Then, a model was built to preoperatively distinguish lung granulomatous nodules (LGNs) from lung adenocarcinoma (LAC) in solitary pulmonary solid nodules (SPSNs). Methods: Data from 841 patients with SPSNs from five...

Federated learning(FL) is a new kind of Artificial Intelligence(AI) aimed at data privacy preservation that builds on decentralizing the training data for the deep learning model. This new technique of data security and privacy sheds light on many critical domains with highly sensitive data, including medical image analysis. Developing a strong, sc...

Advances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accu...

Patient-derived xenograft (PDX) models involve the engraftment of tumour tissue in immunocompromised mice and represent an important pre-clinixtcal oncology research. A limitation of non-small cell lung cancer (NSCLC) PDX model derivation in NOD-scid IL2Rgammanull (NSG) mice is that a subset of initial engraftments are of lymphocytic, rather than t...

How similar are two images? In computational pathology, where Whole Slide Images (WSIs) of digitally scanned tissue samples from patients can be multi-gigapixels in size, determination of degree of similarity between two WSIs is a challenging task with a number of practical applications. In this work, we explore a novel strategy based on kernelized...

The variation in histologic staining between different medical centers is one of the most profound challenges in the field of computer-aided diagnosis. The appearance disparity of pathological whole slide images causes algorithms to become less reliable, which in turn impedes the wide-spread applicability of downstream tasks like cancer diagnosis....

Multiple Instance Learning (MIL) and transformers are increasingly popular in histopathology Whole Slide Image (WSI) classification. However, unlike human pathologists who selectively observe specific regions of histopathology tissues under different magnifications, most methods do not incorporate multiple resolutions of the WSIs, hierarchically an...

Synthetic data generation offers a solution to the data scarcity problem in biomedicine where data are often expensive or difficult to obtain. By increasing the dataset size, more powerful and generalizable machine learning models can be trained, improving their performance in clinical decision support systems. The generation of synthetic data for...

Background: The implementation and usage of digital pathology has undergone a huge development in recent years however the area of intraoperative consultation has not yet become digitized or fully investigated. The aim of this study was to explore the possibilities to digitize this area and consistency between the diagnoses based on the digital sli...

The rapid development of Artificial Intelligence (AI) technology accelerates the application of computational pathology in clinical decision-making. Due to the restriction of computing resources and annotation information, it is challenging for AI-based computational pathology methods to effectively process and analyze the gigapixel whole slide ima...

Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the hematoxylin and eosin (H&E)-stained biopsy data with MALDI mass-spectrometric imaging data of the corresponding tissue to determine the cancerous regions and their unique...

Breast cancer is one of the common malignant tumors in women. It seriously endangers women’s life and health. The human epidermal growth factor receptor 2 (HER2) protein is responsible for the division and growth of healthy breast cells. The overexpression of the HER2 protein is generally evaluated by immunohistochemistry (IHC). The IHC evaluation...

Background Telepathology utilizing high-throughput static whole slide image scanners is proposed to address the challenge of limited pathology services in resource-restricted settings. However, the prohibitive equipment costs and sophisticated technologies coupled with large amounts of space to set up the devices make it impractical for use in reso...

Medical Imaging has become a vital technique that has been embraced in the diagnosis and treatment process of cancer. Histopathological slides, which microscopically examine the suspicious tissue, are considered the golden standard for tumor prognosis and diagnosis. This excellent performance caused a sudden and growing interest in digitizing these...

The definitive diagnosis of canine soft-tissue sarcomas (STSs) is based on histological assessment of formalin-fixed tissues. Assessment of parameters, such as degree of differentiation, necrosis score and mitotic score, give rise to a final tumour grade, which is important in determining prognosis and subsequent treatment modalities. However, grad...

Histopathologic evaluation provides clinicians with vital information for accurate quantification of disease. Hematoxylin and Eosin slide staining is the gold standard in histopathology, revealing tissue morphology, structure, and cellular composition. However, variation in materials, equipment, and staining protocols, make histology slide staining...

The tubule index is a vital prognostic measure in breast cancer tumor grading and is visually evaluated by pathologists. In this paper, a computer-aided patch-based deep learning tubule segmentation framework, named Tubule-U-Net, is developed and proposed to segment tubules in Whole Slide Images (WSI) of breast cancer. Moreover, this paper presents...

Computational pathology targets the automatic analysis of Whole Slide Images (WSI). WSIs are high-resolution digitized histopathology images, stained with chemical reagents to highlight specific tissue structures and scanned via whole slide scanners. The application of different parameters during WSI acquisition may lead to stain color heterogeneit...

The development of rapid and accurate Whole Slide Imaging (WSI) has paved the way for the application of Artificial Intelligence (AI) to digital pathology. The availability of WSI in the recent years allowed the rapid development of various AI technologies to blossom. WSI-based digital pathology combined with neural networks can automate arduous an...

Background Large bowel biopsies are one of the commonest types of biopsy specimen. We describe a service evaluation study to test the feasibility of using artificial intelligence (AI) to triage large bowel biopsies from a reporting backlog and prioritize those that require more urgent reporting. Methods The pathway was developed in the UK by Natio...

Fouad S Alchami,1,2 Zafar Iqbal,3 Carl Niclas Björkhammer,1 Mohammed O Saeed,3 Ramachandran Ramakrishnan,3 Colin Clelland,1 Fareed Ahmad,3 Adrian Charles1 1Department of Pathology, Sidra Medicine, Doha, Qatar; 2Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, Qatar, Doha, Qatar; 3Imaging Information Systems, Sidra Medicine,...

The World Health Organisation has identified cancer as one of the foremost causes of death globally which reports that nearly one in six deaths is due to cancer. Hence, an early and correct diagnosis is required to assist doctors in selecting the accurate and best treatment option for the patient. Pathological data have huge tumour information that...

Lung adenocarcinoma (LUAD) tumour tissue grows into variable morphological architecture called growth patterns (GPs). The GPs are clinically linked to the biological behaviour of the tumour. However, due to the complex heterogeneity of the tumours, there is high inter-and intra-observer variability in the pathologist reporting of GPs. This paper pr...

Lung adenocarcinoma (LUAD) is one of the most common cancers, and patients’ prognostication is crucial for treatment decisions. Histopathological images are the most generally accessible clinical information, however they have not been employed in clinical settings for prognosis. In t his study, we used WSIs and clinical data from TCGA (training an...

Urinary cytology is a useful, essential diagnostic method in routine urological clinical practice. Liquid-based cytology (LBC) for urothelial carcinoma screening is commonly used in the routine clinical cytodiagnosis because of its high cellular yields. Since conventional screening processes by cytoscreeners and cytopathologists using microscopes i...

The rise in Artificial Intelligence (AI) and deep learning research has shown great promise in diagnosing prostate cancer from whole slide image biopsies. Intelligent application interface for diagnosis is a progressive way to communicate AI results in the medical domain for practical use. This paper aims to suggest a way to integrate state-of-the-...

Deep-learning-based survival prediction can assist doctors by providing additional information for diagnosis by estimating the risk or time of death. The former focuses on ranking deaths among patients based on the Cox model, whereas the latter directly predicts the survival time of each patient. However, it is observed that survival time predictio...

The acquisition of multi-modal biological data for the same sample, such as RNA sequencing and whole slide imaging (WSI), has increased in recent years, enabling studying human biology from multiple angles. However, despite these emerging multi-modal efforts, for the majority of studies only one modality is typically available, mostly due to financ...

Accurate detection of HER2 expression through immunohistochemistry (IHC) is of great clinical significance in the treatment of breast cancer. However, manual interpretation of HER2 is challenging, due to the interobserver variability among pathologists. We sought to explore a deep learning method to predict HER2 expression level and gene status bas...

Existing API approaches usually independently leverage detection or classification models to distinguish allergic pollens from Whole Slide Images (WSIs). However, palynologists tend to identify pollen grains in a progressive learning manner instead of the above one-stage straightforward way. They generally focus on two pivotal problems during polle...

Background: Colorectal and gastric cancer are major causes of cancer-related deaths. In Korea, gastrointestinal (GI) endoscopic biopsy specimens account for a high percentage of histopathologic examinations. Lack of a sufficient pathologist workforce can cause an increase in human errors, threatening patient safety. Therefore, we developed a digit...

Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research; however, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedure and produces massive data that capture tumor histological details at high r...

Tissue-based sampling and diagnosis are defined as the extraction of information from certain limited spaces and its diagnostic significance of a certain object. Pathologists deal with issues related to tumor heterogeneity since analyzing a single sample does not necessarily capture a representative depiction of cancer, and a tissue biopsy usually...