Tissue diagnostics is the world of pathologists, and it is increasingly becoming digitalised to
leverage the enormous potential of personalised medicine and of stratifying patients …

Digital slide images produced from routine diagnostic histopathological preparations suffer
from variation arising at every step of the processing pipeline. Typically, pathologists …

Objective Broad adoption of digital pathology (DP) is still lacking, and examples for DP
connecting diagnostic, research, and educational use cases are missing. We blueprint a …

In this chapter, we present the concept that digital pathology and artificial intelligence can
add value and speed to a pathologist's diagnosis while striving toward precision medicine …

Image analysis tasks in computational pathology are commonly solved using convolutional
neural networks (CNNs). The selection of a suitable CNN architecture and hyperparameters …

In recent years, computer vision techniques have made large advances in image recognition
and been applied to aid radiological diagnosis. Computational pathology aims to develop …

Computational pathology uses artificial intelligence to enable precision medicine and
decision support systems through the analysis of whole slide images. It has the potential to …

Digital pathology, powered by whole-slide imaging technology, has the potential to
transform the landscape of cancer research and diagnosis. By converting traditional …

In the past decade, advances in precision oncology have resulted in an increased demand
for predictive assays that enable the selection and stratification of patients for treatment. The …

The accelerated adoption of digital pathology and advances in deep learning have enabled
the development of robust models for various pathology tasks across a diverse array of …