Background Deep learning is a state-of-the-art technology that has rapidly become the
method of choice for medical image analysis. Its fast and robust object detection …

Whole Slide Images (WSI) are widely used in histopathology for research and the diagnosis
of different types of cancer. The preparation and digitization of histological tissues leads to …

Despite increasing numbers of regulatory approvals, deep learning-based computational
pathology systems often overlook the impact of demographic factors on performance …

The field of medicine is undergoing rapid digital transformation. Pathologists are now
striving to digitize their data, workflows, and interpretations, assisted by the enabling …

Digital pathology had a recent growth, stimulated by the implementation of digital whole
slide images (WSIs) in clinical practice, and the pathology field faces shortage of …

With the advent of whole slide image (WSI) scanners, pathology is undergoing a digital
revolution. Simultaneously, with the development of image analysis algorithms based on …

This article proposes a novel concept for a two-step Ki-67/lymphocytes classification cell
detection pipeline on Ki-67 stained histopathological slides utilizing commonly available …

We present an innovative method for rapidly segmenting haematoxylin and eosin (H&E)-
stained tissue in whole-slide images (WSIs) that eliminates a wide range of undesirable …

The use of digitized data in pathology research is rapidly increasing. The whole slide image
(WSI) is an indispensable part of the visual examination of slides in digital pathology and …

Accurate assessment of HER2 expression in tumor tissue is crucial for determining HER2-
targeted treatment options. Nevertheless, pathologists' assessments of HER2 status are less …