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001034875 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-07621
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001034875 041__ $$aEnglish
001034875 1001_ $$0P:(DE-Juel1)185083$$aRaimondo, Federico$$b0$$eCorresponding author
001034875 1112_ $$aInternational Symposium of Biomedical Imaging 2024$$cAthens$$d2024-05-27 - 2024-05-30$$gISBI 2024$$wGreece
001034875 245__ $$aFASTGPR: DIVIDE-AND-CONQUER TECHNIQUE IN NEUROIMAGING DATA SHORTENS TRAINING TIME AND IMPROVES ACCURACY
001034875 260__ $$c2024
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001034875 520__ $$aGaussian process regression (GPR) has shown great potential for studying healthy aging and disease via brain-age prediction (BAP) using structural MRI[1].A big drawback of GPR is the training complexity which is an O(N^3) operation (N=number of data points).The need for expansive datasets and the high dimensionality of MRI data, renders the training of GPR impractical with conventional computing resources.We investigated whether a divide-and-conquer approach can be used together with the GPR model
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001034875 7001_ $$0P:(DE-Juel1)180946$$aAntonopoulos, Georgios$$b1$$eCorresponding author
001034875 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b2
001034875 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh$$b3
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001034875 9141_ $$y2024
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