Federated learning improves site performance in multicenter deep learning without data sharing

Sarma, K. V., Harmon, S., Sanford, T., Roth, H. R., Xu, Z., Tetreault, J., Xu, D., Flores, M. G., Raman, A. G., Kulkarni, R., Wood, B. J., Choyke, P. L., Priester, A. M., Marks, L. S., Raman, S. S., Enzmann, D., Turkbey, B., Speier, W., and Arnold, C. W..

Journal of the American Medical Informatics Association · feb 2021

BibTeX

@article{Sarma2021,
  author = {Sarma, K. V. and Harmon, S. and Sanford, T. and Roth, H. R. and Xu, Z. and Tetreault, J. and Xu, D. and Flores, M. G. and Raman, A. G. and Kulkarni, R. and Wood, B. J. and Choyke, P. L. and Priester, A. M. and Marks, L. S. and Raman, S. S. and Enzmann, D. and Turkbey, B. and Speier, W. and Arnold, C. W.},
  doi = {10.1093/jamia/ocaa341},
  issn = {1067-5027},
  journal = {Journal of the American Medical Informatics Association},
  keywords = {deep learning,federated learning,generalizability,privacy,prostate},
  month = feb,
  publisher = {Oxford University Press (OUP)},
  title = {{Federated learning improves site performance in multicenter deep learning without data sharing}},
  url = {https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocaa341/6127556},
  year = {2021},
}

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