The success of image classification depends on copious annotated images for training. Annotating histopathology images is costly and laborious. Although several successful self-supervised representation learning approaches have been introduced, they...
Histopathology is the study of tissue structure under the microscope to determine if the cells are normal or abnormal. Histopathology is a very important exam that is used to determine the patients’ treatment plan. The classification of histopa...
Breast cancer is one of the deadliest diseases worldwide among women. Early diagnosis and proper treatment can save many lives. Breast image analysis is a popular method for detecting breast cancer. Computer-aided diagnosis of breast images helps rad...
Accurate classification of breast cancer histopathology images is critical for early diagnosis and treatment planning. Yet, conventional deep learning models face significant challenges under limited annotation scenarios due to their reliance on larg...
Accurate and efficient detection of cell nuclei is an important step towards the development of a pathology-based Computer Aided Diagnosis. Generally, high-resolution histopathology images are very large, in the order of billion pixels, therefore nuc...
The automated assessment of tumors in medical image analysis encounters challenges due to the resemblance of colon and lung tumors to non-mitotic nuclei and their heteromorphic characteristics. An accurate assessment of tumor nuclei presence is cruci...
The definitive diagnosis of histology specimen images is largely based on the radiologist’s comprehensive experience; however, due to the fine to the coarse visual appearance of such images, experts often disagree with their assessments. Sophisticate...
Oral cancer is typically diagnosed through histological examination; however, the primary issue with this type of procedure is tumor heterogeneity, where a subjective aspect of the examination may have a direct effect on the treatment plan for a pati...
Polyploidy, the duplication of the entire genome within a single cell, is a significant characteristic of cells in many tissues, including the liver. The quantification of hepatic ploidy typically relies on flow cytometry and immunofluorescence (IF)...
Oral cancer (OC) remains a major global health concern with survival often limited by late diagnosis. Early and accurate detection is essential to improve patient outcomes and guide treatment decisions. In this study we propose a computer aided diagn...
Accurate segmentation of breast cancer regions in histopathological images is critical for advancing computer-aided diagnostic systems, yet challenges persist due to heterogeneous tissue structures, staining variations, and the need to capture featur...
Separating overlapped nuclei is a significant challenge in histopathology image analysis. Recently published approaches have achieved promising overall performance on nuclei segmentation; however, their performance on separating overlapped nuclei is...
Background/Objectives: Breast cancer is among the most frequently diagnosed cancers and leading cause of mortality worldwide. The accurate classification of breast cancer from the histology photographs is very important for the diagnosis and effectiv...
Accurate nuclear segmentation in histopathology images plays a key role in digital pathology. It is considered a prerequisite for the determination of cell phenotype, nuclear morphometrics, cell classification, and the grading and prognosis of cancer...
Breast cancer has the highest mortality rate. Therefore, histologic imaging evaluations must detect breast cancer early. Traditional methods are time-consuming and limit pathologists’ skills. Breast cancer histopathology picture segmentation is...
Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopa...
With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze stu...
Feature descriptors in histopathological images are an important challenge for the implementation of Content-Based Image Retrieval (CBIR) systems, an essential tool to support pathologists. Deep learning models like Convolutional Neural Networks and...
Histopathological imaging has been routinely conducted in cancer diagnosis and recently used for modeling other cancer outcomes/phenotypes such as prognosis. Clinical/environmental factors have long been extensively used in cancer modeling. However,...
CNN-based image processing has been actively applied to histopathological analysis to detect and classify cancerous tumors automatically. However, CNN-based classifiers generally predict a label with overconfidence, which becomes a serious problem in...
The aim of this study was to explore the application of computational models for the analysis of histopathological images in the context of colon cancer. A comprehensive dataset of colon cancer images annotated into eight distinct categories based on...
Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is time and resource-consuming and very challenging even for experienced pathologists, resulting in inter-obser...
Nuclei segmentation is a key task in digital histopathology, highlighting important aspects of nuclear morphology and topology in many cancer-related evaluations and studies. Variability in nuclear appearance both within and across different organs,...
Histopathology refers to the examination by a pathologist of biopsy samples. Histopathology images are captured by a microscope to locate, examine, and classify many diseases, such as different cancer types. They provide a detailed view of different...
The automated classification of breast cancer histopathological images is one of the important tasks in computer-aided diagnosis systems (CADs). Due to the characteristics of small inter-class and large intra-class variances in breast cancer histopat...
As an effective computer-aided diagnostic tool, deep learning has been successfully applied to the classification of breast histopathological images. However, the performance of the deep model is data-driven, and it is difficult to obtain satisfied r...
Histopathological images play a crucial role in diagnosing skin cancer. However, due to the very large size of digital histopathological images (typically in the order of billion pixels), manual image analysis is tedious and time-consuming. Therefore...
Proteomics data encode molecular features of diagnostic value and accurately reflect key underlying biological mechanisms in cancers. Histopathology imaging is a well-established clinical approach to cancer diagnosis. The predictive relationship betw...
Cancer prognosis is of essential interest, and extensive research has been conducted searching for biomarkers with prognostic power. Recent studies have shown that both omics profiles and histopathological imaging features have prognostic power. Ther...
Retrieving histopathological images can assist in the recognition and treatment planning of several diseases. Nevertheless, high-dimensional features can make this process complex and inefficient. These challenges can be addressed by encoding the fea...
The extended utilization of digitized Whole Slide Images is transforming the workflow of traditional clinical histopathology to the digital era. The ongoing transformation has demonstrated major potentials towards the exploitation of Machine Learning...
Introduction and Background: Despite fast developments in the medical field, histological diagnosis is still regarded as the benchmark in cancer diagnosis. However, the input image feature extraction that is used to determine the severity of cancer a...
Automatic classification of histopathological images plays an important role in computer-aided diagnosis systems. The automatic classification model of histopathological images based on deep neural networks has received widespread attention. However,...
Imaging-based multi-omics derived from digital histopathology provides a valuable approach for characterizing tumor heterogeneity from routine clinical specimens. However, robust multi-cancer histopathological analysis remains challenging due to pron...
Deep learning has achieved remarkable progress in medical image analysis, but its effectiveness heavily relies on large-scale and well-annotated datasets. However, assembling a large-scale dataset of annotated histopathological images is challenging...
In the medical field, hematoxylin and eosin (H&E)-stained histopathology images of cell nuclei analysis represent an important measure for cancer diagnosis. The most valuable aspect of the nuclei analysis is the segmentation of the different nucl...
Background: Glioblastoma (GBM) is one of the most common malignant primary brain tumors, which accounts for 60–70% of all gliomas. Conventional diagnosis and the decision of post-operation treatment plan for glioblastoma is mainly based on the...
24 May 2021
Background: Imaging methods and the most appropriate criteria to be used for detecting and evaluating response to oncological treatments depend on the pathology and anatomical site to be treated and on the treatment to be performed. This document pro...
Background/Objectives: Deep convolutional neural networks achieve strong histopathological image-classification performance but often require substantial computational resources. This study proposes DSR-VGG, a lightweight Depthwise Separable Residual...
The early detection and intervention of oral squamous cell carcinoma (OSCC) using histopathological images are crucial for improving patient outcomes. The current literature for identifying OSCC predominantly relies on models pre-trained on ImageNet...
Histopathology image analysis is considered as a gold standard for the early diagnosis of serious diseases such as cancer. The advancements in the field of computer-aided diagnosis (CAD) have led to the development of several algorithms for accuratel...
Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images....
Breast cancer is the major cause behind the death of women worldwide and is responsible for several deaths each year. Even though there are several means to identify breast cancer, histopathological diagnosis is now considered the gold standard in th...
It is well-known that accurate classification of histopathological images is essential for effective diagnosis of colorectal cancer. Our study presents three attention-based decision fusion models that combine pre-trained CNNs (Inception V3, Xception...
The classification of whole slide images (WSIs) provides physicians with an accurate analysis of diseases and also helps them to treat patients effectively. The classification can be linked to further detailed analysis and diagnosis. Deep learning (D...
Breast cancer is the second most common cancer worldwide, primarily affecting women, while histopathological image analysis is one of the possibile methods used to determine tumor malignancy. Regarding image analysis, the application of deep learning...
Epidermal growth factor receptor 2 (HER2) has been widely recognized as one of the targets for bladder cancer immunotherapy. The key to implementing personalized treatment for bladder cancer patients lies in achieving rapid and accurate diagnosis. To...
Content-Based Histopathological Image Retrieval (CBHIR) is a search technique based on the visual content and histopathological features of whole-slide images (WSIs). CBHIR tools assist pathologists to obtain a faster and more accurate cancer diagnos...
In order to recognize breast cancer histopathological images, this article proposed a combined model consisting of a pyramid gray level co-occurrence matrix (PGLCM) feature extraction model and an incremental broad learning (IBL) classification model...
Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usual...
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