Compressed Online Dictionary Learning for Fast Resting-State fMRI Decomposition

Abstract : We present a method for fast resting-state fMRI spatial decompositions of very large datasets, based on the reduction of the temporal dimension before applying dictionary learning on concatenated individual records from groups of subjects. Introducing a measure of correspondence between spatial decompositions of rest fMRI, we demonstrates that time-reduced dictionary learning produces result as reliable as non-reduced decompositions. We also show that this reduction significantly improves computational scalability.
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Communication dans un congrès
International Symposium on Biomedical Imaging (ISBI 2016) "From Nano to Macro", Apr 2016, Prague, Czech Republic. IEEE, pp.1282-1285, 2016, 13th International Symposium on Biomedical Imaging (ISBI). <http://ieeexplore.ieee.org/document/7493501/>. <10.1109/ISBI.2016.7493501>
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https://hal.archives-ouvertes.fr/hal-01271033
Contributeur : Arthur Mensch <>
Soumis le : jeudi 12 mai 2016 - 12:52:35
Dernière modification le : samedi 18 février 2017 - 01:14:37

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Arthur Mensch, Gaël Varoquaux, Bertrand Thirion. Compressed Online Dictionary Learning for Fast Resting-State fMRI Decomposition. International Symposium on Biomedical Imaging (ISBI 2016) "From Nano to Macro", Apr 2016, Prague, Czech Republic. IEEE, pp.1282-1285, 2016, 13th International Symposium on Biomedical Imaging (ISBI). <http://ieeexplore.ieee.org/document/7493501/>. <10.1109/ISBI.2016.7493501>. <hal-01271033v2>

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