In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …

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

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

Background Gastrointestinal cancers account for approximately 20% of all cancer diagnoses
and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence–based …

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 …

Background Microsatellite instability (MSI)/mismatch repair deficiency (dMMR) is a key
genetic feature which should be tested in every patient with colorectal cancer (CRC) …

Learning informative representations is crucial for classification and prediction tasks on
histopathological images. Due to the huge image size, whole-slide histopathological image …

Colorectal cancer (CRC) is the second most common cancer in women and the third most
common in men, with an increasing incidence. Pathology diagnosis complemented with …

Background With the development of digital pathology and the renewal of deep learning
algorithm, artificial intelligence (AI) is widely applied in tumor pathology. Previous …