Motivation Digitization of pathology laboratories through digital slide scanners and
advances in deep learning approaches for objective histological assessment have resulted …

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

The interpretation of digitized histopathology images has been transformed thanks to
artificial intelligence (AI). End-to-end AI algorithms can infer high-level features directly from …

Background: Histopathology is a gold standard for cancer diagnosis. It involves extracting
tissue specimens from suspicious areas to prepare a glass slide for a microscopic …

Make deep learning algorithms in computational pathology more reproducible and reusable |
Nature Medicine Skip to main content Thank you for visiting nature.com. You are using a browser …

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

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically
approved applications use this technology. Most approaches, however, predict categorical …

To handle the large scale of whole slide images in computational pathology, most
approaches first tessellate the images into smaller patches, extract features from these …

Deep learning techniques, such as convolutional neural networks (CNNs), generative
adversarial networks (GANs), and graph neural networks (GNNs) have, over the past …

There is a lot of recent interest in the field of computational pathology, as many algorithms
are introduced to detect, for example, cancer lesions or molecular features. However, there …