Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …

Precision oncology relies on the identification of targetable molecular alterations in tumor
tissues. In many tumor types, a limited set of molecular tests is currently part of standard …

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology
slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other …

The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep
learning can predict genetic alterations from pathology slides, but it is unclear how well …

Histopathology plays a fundamental role in the diagnosis and subtyping of solid tumors and
has become a cornerstone of modern precision oncology. Histopathological evaluation is …

Deep learning based medical image recognition systems often require a substantial amount
of training data with expert annotations, which can be expensive and time-consuming to …

Artificial intelligence (AI) is widely used to analyze gastrointestinal (GI) endoscopy image
data. AI has led to several clinically approved algorithms for polyp detection, but application …

Background Fast and accurate diagnostics are key for personalised medicine. Particularly in
cancer, precise diagnosis is a prerequisite for targeted therapies, which can prolong lives. In …

Hematoxylin and eosin (H&E) stained slides are widely used in disease diagnosis.
Remarkable advances in deep learning have made it possible to detect complex molecular …