Abstract Machine learning has revolutionized every facet of human life, while also becoming
more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare …

Modern machine learning increasingly supports paradigms that are multi-institutional (using
data from multiple institutions during training) or cross-institutional (using models from …

Federated learning (FL) is a technique for distributed machine learning that enables the use
of siloed and distributed data. With FL, individual machine learning models are trained …

The current push towards interoperability drives companies to collaborate through process
choreographies. At the same time, they face a jungle of continuously changing regulations …

Abstract The implementation of Artificial Intelligence (AI) still faces significant hurdles and
one key factor is the access to data. One approach that could support that is federated …

Motivation In multi-cohort machine learning studies, it is critical to differentiate between
effects that are reproducible across cohorts and those that are cohort-specific. Multi-task …

Background Institutions interested in collaborative machine learning to enhance healthcare
may be deterred by privacy concerns. Decentralized federated learning is a privacy …

Machine learning models trained in medical imaging can help in the early detection,
diagnosis, and prognosis of the disease. However, it confronts two major obstacles: deep …

We explore the integration of domain knowledge graphs into Deep Learning for improved
interpretability and explainability using Graph Neural Networks (GNNs). Specifically, a …