Hematoxylin-and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis
of cancer. In recent years, development of deep learning-based methods in computational …

Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to
benefit oncology through interpretable methods that require only one overall label per …

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …

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 …

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

Computational pathology is revolutionizing the field of pathology by integrating advanced
computer vision and machine learning technologies into diagnostic workflows. It offers …

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

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

Recently, a wide spectrum of artificial intelligence (AI)–based applications in the broader
categories of digital pathology, biomarker development, and treatment have been explored …