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

Traditional pathology approaches have played an integral role in the delivery of diagnosis,
semi-quantitative or qualitative assessment of protein expression, and classification of …

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 …

Abstract Machine learning model deployment in clinical practice demands real-time risk
assessment to identify situations in which the model is uncertain. Once deployed, models …

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

Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of
digital pathology (DP) and an increase in computational power have led to the development …

Mitosis counting of biopsies is an important biomarker for breast cancer patients, which
supports disease prognostication and treatment planning. Developing a robust mitotic cell …

Pathologists worldwide are facing remarkable challenges with increasing workloads and
lack of time to provide consistently high-quality patient care. The application of artificial …

Immunotherapy by immune checkpoint inhibitors has become a standard treatment strategy
for many types of solid tumors. However, the majority of patients with cancer will not …

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