A Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations
Published in Lecture Notes in Computer Science, 2021
In this paper we propose a new Bayesian framework for federated learning of heterogeneous multi-modal biomedical data. An application to a large database (including medical imaging and clinical scores) on patients with Alzheimer disease shows that the proposed model allows high quality data reconstruction, compared to current auto-encoding methods and federated learning schemes.
Recommended citation: Balelli I., Silva S., Lorenzi M. (2021). A Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations. In: Feragen A., Sommer S., Schnabel J., Nielsen M. (eds) Information Processing in Medical Imaging. IPMI 2021. Lecture Notes in Computer Science, vol 12729. Springer, Cham. https://doi.org/10.1007/978-3-030-78191-0_54