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

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

18 January 2021
Jean-Francois Rajotte
S. Mukherjee
Caleb Robinson
Anthony Ortiz
Christopher West
J. L. Ferres
R. Ng
    FedML
    MedIm
ArXivPDFHTML

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

3 / 3 papers shown
Title
Generating multivariate time series with COmmon Source CoordInated GAN
  (COSCI-GAN)
Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN)
Ali Seyfi
Jean-Francois Rajotte
Raymond T. Ng
AI4TS
16
27
0
27 May 2022
MACE: A Flexible Framework for Membership Privacy Estimation in
  Generative Models
MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models
Yixi Xu
S. Mukherjee
Xiyang Liu
Shruti Tople
Rahul Dodhia
J. L. Ferres
MIACV
19
11
0
11 Sep 2020
The Future of Digital Health with Federated Learning
The Future of Digital Health with Federated Learning
Nicola Rieke
Jonny Hancox
Wenqi Li
Fausto Milletari
H. Roth
...
Ronald M. Summers
Andrew Trask
Daguang Xu
Maximilian Baust
M. Jorge Cardoso
OOD
174
1,705
0
18 Mar 2020
1