Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …

In recent years, machine learning (ML) has become a key enabling technology for the
sciences and industry. Especially through improvements in methodology, the availability of …

Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic
diagnosis of cancer is increasing as personalized cancer therapy requires accurate …

Rational design of compounds with specific properties requires understanding and fast
evaluation of molecular properties throughout chemical compound space—the huge set of …

Recent advances in cancer research and diagnostics largely rely on new developments in
microscopic or molecular profiling techniques, offering high levels of detail with respect to …

In recent decades, the increasing interest in the field of immunotherapy has fostered an
intense investigation of the breast cancer (BC) immune microenvironment. In this context …

Immunotherapies play critical roles in cancer treatment. However, given that only a few
patients respond to immune checkpoint blockades and other immunotherapeutic strategies …