?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Federated+Learning+in+Distributed+Medical+Databases%3A+Meta-Analysis+of+Large-Scale+Subcortical+Brain+Data&rft.creator=Silva%2C+S&rft.creator=Gutman%2C+B&rft.creator=Romero%2C+E&rft.creator=Thompson%2C+P&rft.creator=Altmann%2C+A&rft.creator=Lorenzi%2C+M&rft.description=At+this+moment%2C+databanks+worldwide+contain+brain+images+of+previously+unimaginable+numbers.+Combined+with+developments+in+data+science%2C+these+massive+data+provide+the+potential+to+better+understand+the+genetic+underpinnings+of+brain+diseases.+However%2C+different+datasets%2C+which+are+stored+at+different+institutions%2C+cannot+always+be+shared+directly+due+to+privacy+and+legal+concerns%2C+thus+limiting+the+full+exploitation+of+big+data+in+the+study+of+brain+disorders.+Here+we+propose+a+federated+learning+framework+for+securely+accessing+and+meta-analyzing+any+biomedical+data+without+sharing%0D%0Aindividual+information.+We+illustrate+our+framework+by+investigating+brain+structural+relationships+across+diseases+and+clinical+cohorts.+The+framework+is+first+tested+on+synthetic+data+and+then+applied+to+multi-centric%2C+multi-database+studies+including+ADNI%2C+PPMI%2C%0D%0AMIRIAD+and+UK+Biobank%2C+showing+the+potential+of+the+approach+for+further+applications+in+distributed+analysis+of+multi-centric+cohorts.&rft.subject=Federated+learning%2C+distributed+databases%2C+PCA%2C+SVD%2C%0D%0Ameta-analysis%2C+brain+disease&rft.publisher=IEEE+Xplore&rft.contributor=Linguraru%2C+Marius+George&rft.contributor=Grisan%2C+Enrico&rft.date=2019&rft.type=Proceedings+paper&rft.publisher=IEEE+International+Symposium+on+Biomedical+Imaging&rft.language=eng&rft.source=+++++In%3A+Linguraru%2C+Marius+George+and+Grisan%2C+Enrico%2C+(eds.)+Proceedings+to+IEEE+16th+International+Symposium+on+Biomedical+Imaging+(ISBI+2019)%2C+8-11+April+2019%2C+Venice%2C+Italy.++++IEEE+Xplore%3A+New+York%2C+USA.+(2019)++++(In+press).++&rft.format=text&rft.identifier=https%3A%2F%2Fdiscovery.ucl.ac.uk%2Fid%2Feprint%2F10069399%2F1%2F1810.08553v2.pdf&rft.identifier=https%3A%2F%2Fdiscovery.ucl.ac.uk%2Fid%2Feprint%2F10069399%2F&rft.rights=open