Tissue biomarkers are crucial for cancer diagnosis, prognosis assessment and treatment
planning. However, there are few known biomarkers that are robust enough to show true …

Deep learning can mine clinically useful information from histology. In gastrointestinal and
liver cancer, such algorithms can predict survival and molecular alterations. Once pathology …

Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past
decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of …

The integration of multi-modal data, such as histopathological images and genomic data, is
essential for understanding cancer heterogeneity and complexity for personalized …

Background: Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a
wide range of applications in image analysis, ranging from automated segmentation to …

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically
approved applications use this technology. Most approaches, however, predict categorical …

Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers.
However, the growing number of these complex biomarkers tends to increase the cost and …

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been
applied to many areas in different domains such as health care and drug design. Cancer …

Deriving interpretable prognostic features from deep-learning-based prognostic
histopathology models remains a challenge. In this study, we developed a deep learning …

Background The objective of this comprehensive pan-cancer study is to evaluate the
potential of deep learning (DL) for molecular profiling of multi-omic biomarkers directly from …