Importance Application of deep learning algorithms to whole-slide pathology images can
potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of …

Breast cancer is the leading cause of death for women globally. In clinical practice,
pathologists visually scan over enormous amounts of gigapixel microscopic tissue slide …

The frozen section (FS) diagnoses of pathology experts are used in China to determine
whether sentinel lymph nodes of breast cancer have metastasis during operation. Direct …

The gold standard for breast cancer diagnosis, treatment, and management is the
histological analysis of a suspected section. Histopathology consists in analyzing the …

Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in
digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the …

Background The presence of lymph node metastases is one of the most important factors in
breast cancer prognosis. The most common way to assess regional lymph node status is the …

The International Symposium on Biomedical Imaging (ISBI) held a grand challenge to
evaluate computational systems for the automated detection of metastatic breast cancer in …

This paper proposes a profound learning approach in Whole-slide images of breast cancer
(WSI) for automatic detection and visual study of invasive ductal cancer (IDC) tissue regions …

Convolutional neural networks have led to significant breakthroughs in the domain of
medical image analysis. However, the task of breast cancer segmentation in whole-slide …

Simple Summary In recent years many successful models have been developed to perform
various tasks in digital histopathology, yet, there is still a reluctance to fully embrace the new …