Silva, S;
Gutman, B;
Romero, E;
Thompson, P;
Altmann, A;
Lorenzi, M;
(2019)
Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data.
In: Linguraru, Marius George and Grisan, Enrico, (eds.)
Proceedings to IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 8-11 April 2019, Venice, Italy.
IEEE Xplore: New York, USA.
(In press).
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Abstract
At this moment, databanks worldwide contain brain images of previously unimaginable numbers. Combined with developments in data science, these massive data provide the potential to better understand the genetic underpinnings of brain diseases. However, different datasets, which are stored at different institutions, cannot always be shared directly due to privacy and legal concerns, 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 individual 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, multi-database studies including ADNI, PPMI, MIRIAD and UK Biobank, showing the potential of the approach for further applications in distributed analysis of multi-centric cohorts.
Type: | Proceedings paper |
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Title: | Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data |
Event: | IEEE International Symposium on Biomedical Imaging |
Location: | Venice, Italy |
Dates: | 08 April 2019 - 11 April 2019 |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | https://biomedicalimaging.org/2019/ |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Federated learning, distributed databases, PCA, SVD, meta-analysis, brain disease |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
URI: | https://discovery.ucl.ac.uk/id/eprint/10069399 |




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