PURPOSE Deep learning (DL), a class of approaches involving self-learned discriminative
features, is increasingly being applied to digital pathology (DP) images for tasks such as …

Background: Deep learning (DL) is a representation learning approach ideally suited for
image analysis challenges in digital pathology (DP). The variety of image analysis tasks in …

Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …

Deep learning (DL) has achieved state-of-the-art performance in many digital pathology
analysis tasks. Traditional methods usually require hand-crafted domain-specific features …

Histopathology images; microscopy images of stained tissue biopsies contain fundamental
prognostic information that forms the foundation of pathological analysis and diagnostic …

Background Digitized histopathological tissue slides and genomics profiling data are
available for many patients with solid tumors. In the last 5 years, Deep Learning (DL) has …

Background An increasing number of studies within digital pathology show the potential of
artificial intelligence (AI) to diagnose cancer using histological whole slide images, which …

Digital pathology has seen a proliferation of deep learning models in recent years, but many
models are not readily reusable. To address this challenge, we developed WSInfer: an open …

Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is
mastering computer vision tasks, its application to digital pathology is natural, with the …

Motivation Digital pathology supports analysis of histopathological images using deep
learning methods at a large-scale. However, applications of deep learning in this area have …