ALTMANN , Andre

While data in healthcare is produced in quantities never imagined before, the feasibility of clinical studies is often hindered by the problem of data access and transfer, especially regarding privacy concerns. Federated learning allows privacy-preserving data analyses using decentralized optimization approaches keeping data securely...

This chapter focuses on the joint modeling of heterogeneous information, such as imaging, clinical, and biological data. This kind of problem requires to generalize classical uni-and multivariate association models to account for complex data structure and interactions, as well as high data dimensionality. Typical approaches are essentially...

International audience

This document contains the acknowledgements for the work: Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data. We extend our thanks to all the collaborators and founders that make this work possible.

International audience

The joint modeling of brain imaging information and genetic data is a promising research avenue to highlight the functional role of genes in determining the pathophysiological mechanisms of Alzheimer's disease (AD). However, since genome-wide association (GWA) studies are essentially limited to the exploration of statistical correlations...