• "Federated learning in biomedical data: application to brain imaging and genetics analysis", Marco Lorenzi (UCA, Inria-Epione)
When dealing with distributed biomedical data, meta-analysis methods are classically used to analyse cohorts distributed in different centres. Standard approaches to meta-analysis rely on univariate testing, by sharing test statistics or effect sizes. However, when the features to be analysed are in the order of millions (e.g. in case of medical images), the mass-univariate paradigm is prone to statistical limitations, such as the multiple comparisons problem, as well as interpretability issues when features are highly correlated. All in all, mass-univariate results often lack stability and reproducibility.
To overcome these limitations, we propose to reformulate multivariate analysis methods, such as dimensionality reduction and regression, within a federated paradigm. Our strategy consists in estimating independent sub-models at each centre, whose parameters are subsequently shared. Importantly, our formulation does not require any data exchange, and involves a very limited amount of information transfer across centres. We already successfully turned our research methods into usable and accessible software, that will soon be applied to the analysis of imaging-genetics data from the large-scale multicentric consortium ENIGMA (http://enigma.ini.usc.edu/). This modelling paradigm may open the way to the effective use of advanced statistical learning methods in today’s complex healthcare scenario.
Lorenzi, Gutman, Thompson, Alexander, Ourselin, & Altmann (2017): "Secure multivariate large-scale multi-centric analysis through on-line learning: an imaging genetics case study", 12th International Symposium on Medical Information Processing and Analysis
Silva, Gutman, Romero, Thompson, Altmann, Lorenzi (2019): "Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data", International Symposium on Biomedical Imaging