Abstract
Driven by the Deep Learning (DL) revolution, Artificial intelligence
(AI) has become a fundamental tool for many Bio-Medical tasks, including
AI-assisted diagnosis. These include analysing and classifying images
(2D and 3D), where, for some tasks, DL exhibits superhuman performance.
Diagnostic imaging, however, is not the only diagnostic tool. Tabular
data, such as personal data, vital signs, and genomic/blood tests, are
commonly collected for every patient entering a clinical institution.
However, it is rarely considered in DL pipelines, although it carries
diagnostic information. The training of DL models requires large
datasets, so large that every institution might need more data that
should be pooled from different sites. Data pooling generates newfound
concerns about data access and movement across other institutions
spawning multiple dimensions, such as performance, energy efficiency,
privacy, criticality, and security. Federated Learning (FL) is a
cooperative learning paradigm aiming at addressing these concerns by
moving models instead of data across different institutions. This paper
proposes a Federated multi-input model that leverages images and tabular
data, providing a proof of concept of the feasibility of multi-input FL
architectures. The proposed model was evaluated on two showcases: the
prognosis of CoViD-19 disease and the patients’ stratification for
Alzheimer’s disease. Results show that enabling multi-input
architectures in the FL framework allows for improving the performance
regarding both accuracy and generalizability with respect to
non-federated models while ensuring security and data protection
peculiar to FL.