Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary-Reference-Cited by-同舟云学术

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Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary

Published:2021-09-09 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Conference on Information Technology for Social Good
language:
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Author:

Rajotte Jean-Francois¹,Mukherjee Sumit²,Robinson Caleb²,Ortiz Anthony²,West Christopher³,Ferres Juan M. Lavista²,Ng Raymond T.⁴

Affiliation:

1. Data Science Institute, University of British Columbia, Vancouver, British Columbia, Canada

2. AI for Good Research Lab, Microsoft, Redmond, USA

3. Department of Computer Science, University of British Columbia, Vancouver, Canada

4. Data Science Institute, University of British Columbia, Vancouver, Canada

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3462203.3475875

Reference30 articles.

1. Deep Learning with Differential Privacy

2. Differential Privacy for Classifier Evaluation

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