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Synthetic Observational Health Data with GANs: from slow adoption to a
  boom in medical research and ultimately digital twins?

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

27 May 2020
Jeremy Georges-Filteau
Elisa Cirillo
    SyDa
    AI4CE
ArXivPDFHTML

Papers citing "Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?"

5 / 5 papers shown
Title
A review of Generative Adversarial Networks for Electronic Health
  Records: applications, evaluation measures and data sources
A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources
Ghadeer O. Ghosheh
Jin Li
T. Zhu
12
31
0
14 Mar 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
11
11
0
11 Sep 2020
Threats to Federated Learning: A Survey
Threats to Federated Learning: A Survey
Lingjuan Lyu
Han Yu
Qiang Yang
FedML
186
432
0
04 Mar 2020
Secure and Robust Machine Learning for Healthcare: A Survey
Secure and Robust Machine Learning for Healthcare: A Survey
A. Qayyum
Junaid Qadir
Muhammad Bilal
Ala I. Al-Fuqaha
AAML
OOD
31
371
0
21 Jan 2020
Generating Multi-label Discrete Patient Records using Generative
  Adversarial Networks
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
E. Choi
Siddharth Biswal
B. Malin
J. Duke
Walter F. Stewart
Jimeng Sun
SyDa
GAN
145
568
0
19 Mar 2017
1