Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …

Given the recent success of artificial intelligence (AI) in computer vision applications, many
pathologists anticipate that AI will be able to assist them in a variety of digital pathology …

Computational pathology refers to applying deep learning techniques and algorithms to
analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led …

In this white paper, experts from the Digital Pathology Association (DPA) define terminology
and concepts in the emerging field of computational pathology, with a focus on its …

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

Background: Educational use cases for digital pathology have been a core use case for
many years. However, only a few educational focused software platforms have been …

Computational pathology (CPath) algorithms detect, segment or classify cancer in whole
slide images, approaching or even exceeding the accuracy of pathologists. Challenges …

Recent progress in computational pathology has been driven by deep learning. While code
and data availability are essential to reproduce findings from preceding publications …

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 …

Aims Artificial intelligence (AI) provides a powerful tool to extract information from digitised
histopathology whole slide images. During the last 5 years, academic and commercial …