AnomiGAN: Generative Adversarial Networks for Anonymizing Private Medical Data-Reference-Cited by-同舟云学术

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AnomiGAN: Generative Adversarial Networks for Anonymizing Private Medical Data

Published:2019-11-27 Issue: Volume: Page:
ISSN:
Container-title:Biocomputing 2020
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Author:

Bae Ho¹,Jung Dahuin²,Choi Hyun-Soo²,Yoon Sungroh¹²³⁴⁵

Affiliation:

1. Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea

2. Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea

3. Biological Sciences, Seoul National University, Seoul 08826, Korea

4. ASRI and INMC, Seoul National University, Seoul 08826, Korea

5. Institute of Engineering Research, Seoul National University, Seoul 08826, Korea

Publisher

WORLD SCIENTIFIC

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Comprehensive Survey Integrating Scientometric Analysis and ML approaches for Data Protection;2024-01-18

2. Evaluating three strategies of genome‐wide association analysis for integrating data from multiple populations;Animal Genetics;2024-01-07

3. Time-series Anonymization of Tabular Health Data using Generative Adversarial Network;2023 International Joint Conference on Neural Networks (IJCNN);2023-06-18

4. Class Activation Maps for the disentanglement and occlusion of identity attributes in medical imagery;2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI);2022-09-27

5. Patient identification methods based on medical imagery and their impact on patient privacy and open medical data;2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS);2022-07

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