Medical imaging involves high-dimensional data, yet their acquisition is obtained for limited samples. Multivariate predictive models have become popular in the last decades to fit some external variables from imaging data, and standard algorithms yield point estimates of the model parameters. It is however challenging to attribute confidence to these parameter estimates, which makes solutions hardly trustworthy.\par In this talk, I will present a new algorithm that assesses parameters statistical significance and that can scale even when the number of predictors $p \geq 10^5$ is much higher than the number of samples $n \leq 10^3$ , by leveraging structure among features. Our algorithm combines three main in-gredients: a powerful inference procedure for linear models --the so-called Desparsified Lasso-- feature clustering and an ensembling step. We first establish that Desparsified Lasso alone cannot handle $n \ll p$ regimes; then we demonstrate that the combination of clustering and ensembling provides an accurate solution, whose specificity is controlled. We also demonstrate stability improvements on two neuroimaging datasets.