Advanced image analysis can lead to automated examination to histopatholgy images
which is essential for ob-jective and fast cancer diagnosis. Recently deep learning methods …

Pathology analysis is crucial to precise cancer diagnoses and the succeeding treatment
plan as well. To detect abnormality in histopathology images with prevailing patch-based …

To detect abnormality from histopathology images in a patch-based convolutional neural
network (CNN), spatial context is an important cue. However, whole-slide image (WSI) is …

Histopathological identification of tumor tissue is one of the routine pathological diagnoses
for pathologists. Recently, computational pathology has been successfully interpreted by a …

Digitizing whole-slide imaging in digital pathology has led to the advancement of computer-
aided tissue examination using machine learning techniques, especially convolutional …

Breast cancer diagnosis often requires accurate detection of metastasis in lymph nodes
through Whole-slide Images (WSIs). Recent advances in deep convolutional neural …

The detection of the region of interests (ROIs) on Whole Slide Images (WSIs) is one of the
primary steps in computer-aided cancer diagnosis and grading. Early and accurate …

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 …

Currently, histopathological tissue examination by a pathologist represents the gold
standard for breast lesion diagnostics. Automated classification of histopathological whole …

Visual analysis of solid tissue mounted on glass slides is currently the primary method used
by pathologists for determining the stage, type and subtypes of cancer. Although whole slide …