Skip to Main content Skip to Navigation

Ensembles of models in fMRI : stable learning in large-scale settings

Abstract : In medical imaging, collaborative worldwide initiatives have begun theacquisition of hundreds of Terabytes of data that are made available to thescientific community. In particular, functional Magnetic Resonance Imaging --fMRI-- data. However, this signal requires extensive fitting and noise reduction steps to extract useful information. The complexity of these analysis pipelines yields results that are highly dependent on the chosen parameters.The computation cost of this data deluge is worse than linear: as datasetsno longer fit in cache, standard computational architectures cannot beefficiently used.To speed-up the computation time, we considered dimensionality reduction byfeature grouping. We use clustering methods to perform this task. We introduce a linear-time agglomerative clustering scheme, Recursive Nearest Agglomeration (ReNA). Unlike existing fast agglomerative schemes, it avoids the creation of giant clusters. We then show empirically how this clustering algorithm yields very fast and accurate models, enabling to process large datasets on budget.In neuroimaging, machine learning can be used to understand the cognitiveorganization of the brain. The idea is to build predictive models that are used to identify the brain regions involved in the cognitive processing of an external stimulus. However, training such estimators is a high-dimensional problem, and one needs to impose some prior to find a suitable model.To handle large datasets and increase stability of results, we propose to useensembles of models in combination with clustering. We study the empirical performance of this pipeline on a large number of brain imaging datasets. This method is highly parallelizable, it has lower computation time than the state-of-the-art methods and we show that, it requires less data samples to achieve better prediction accuracy. Finally, we show that ensembles of models improve the stability of the weight maps and reduce the variance of prediction accuracy.
Complete list of metadata

Cited literature [174 references]  Display  Hide  Download
Contributor : Abes Star :  Contact
Submitted on : Tuesday, May 23, 2017 - 1:36:10 PM
Last modification on : Monday, February 10, 2020 - 6:13:43 PM
Long-term archiving on: : Friday, August 25, 2017 - 12:59:28 AM


Version validated by the jury (STAR)


  • HAL Id : tel-01526693, version 1



Andrés Hoyos-Idrobo. Ensembles of models in fMRI : stable learning in large-scale settings. Medical Imaging. Université Paris-Saclay, 2017. English. ⟨NNT : 2017SACLS029⟩. ⟨tel-01526693⟩



Record views


Files downloads