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

With biotechnological advancements, innovative omics technologies are constantly
emerging that have enabled researchers to access multi-layer information from the genome …

Artificial intelligence (AI) can extract visual information from histopathological slides and
yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of …

Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what
dose? Clinical trials' results provide the basis for guidance and support for official protocols …

Artificial intelligence (AI) can predict the presence of molecular alterations directly from
routine histopathology slides. However, training robust AI systems requires large datasets …

Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of
metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative …

Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently,
artificial intelligence approaches, particularly machine learning and deep learning, are …

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

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 …

We propose a Deep learning-based weak label learning method for analyzing whole slide
images (WSIs) of Hematoxylin and Eosin (H&E) stained tumor tissue not requiring pixel-level …