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

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine
pathology slides in colorectal cancer (CRC). However, current approaches rely on …

Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis
and providing biomarkers directly from routine pathology slides. However, AI applications …

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 …

Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to
hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined …

Current diagnosis of glioma types requires combining both histological features and
molecular characteristics, which is an expensive and time-consuming procedure …

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

Computational histopathology is focused on the automatic analysis of rich phenotypic
information contained in gigabyte whole slide images, aiming at providing cancer patients …