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A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs)

Clinical data sharing can facilitate data-driven scientific research, allowing a broader range of questions to be addressed and thereby leading to greater understanding and innovation. However, sharing biomedical data can put sensitive personal information at risk. This is usually addressed by data...

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Autores principales: Sun, Hanxi, Plawinski, Jason, Subramaniam, Sajanth, Jamaludin, Amir, Kadir, Timor, Readie, Aimee, Ligozio, Gregory, Ohlssen, David, Baillie, Mark, Coroller, Thibaud
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10325103/
https://www.ncbi.nlm.nih.gov/pubmed/37410795
http://dx.doi.org/10.1371/journal.pone.0280316
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author Sun, Hanxi
Plawinski, Jason
Subramaniam, Sajanth
Jamaludin, Amir
Kadir, Timor
Readie, Aimee
Ligozio, Gregory
Ohlssen, David
Baillie, Mark
Coroller, Thibaud
author_facet Sun, Hanxi
Plawinski, Jason
Subramaniam, Sajanth
Jamaludin, Amir
Kadir, Timor
Readie, Aimee
Ligozio, Gregory
Ohlssen, David
Baillie, Mark
Coroller, Thibaud
author_sort Sun, Hanxi
collection PubMed
description Clinical data sharing can facilitate data-driven scientific research, allowing a broader range of questions to be addressed and thereby leading to greater understanding and innovation. However, sharing biomedical data can put sensitive personal information at risk. This is usually addressed by data anonymization, which is a slow and expensive process. An alternative to anonymization is construction of a synthetic dataset that behaves similar to the real clinical data but preserves patient privacy. As part of a collaboration between Novartis and the Oxford Big Data Institute, a synthetic dataset was generated based on images from COSENTYX® (secukinumab) ankylosing spondylitis (AS) clinical studies. An auxiliary classifier Generative Adversarial Network (ac-GAN) was trained to generate synthetic magnetic resonance images (MRIs) of vertebral units (VUs), conditioned on the VU location (cervical, thoracic and lumbar). Here, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties along three key metrics: image fidelity, sample diversity and dataset privacy.
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spelling pubmed-103251032023-07-07 A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs) Sun, Hanxi Plawinski, Jason Subramaniam, Sajanth Jamaludin, Amir Kadir, Timor Readie, Aimee Ligozio, Gregory Ohlssen, David Baillie, Mark Coroller, Thibaud PLoS One Research Article Clinical data sharing can facilitate data-driven scientific research, allowing a broader range of questions to be addressed and thereby leading to greater understanding and innovation. However, sharing biomedical data can put sensitive personal information at risk. This is usually addressed by data anonymization, which is a slow and expensive process. An alternative to anonymization is construction of a synthetic dataset that behaves similar to the real clinical data but preserves patient privacy. As part of a collaboration between Novartis and the Oxford Big Data Institute, a synthetic dataset was generated based on images from COSENTYX® (secukinumab) ankylosing spondylitis (AS) clinical studies. An auxiliary classifier Generative Adversarial Network (ac-GAN) was trained to generate synthetic magnetic resonance images (MRIs) of vertebral units (VUs), conditioned on the VU location (cervical, thoracic and lumbar). Here, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties along three key metrics: image fidelity, sample diversity and dataset privacy. Public Library of Science 2023-07-06 /pmc/articles/PMC10325103/ /pubmed/37410795 http://dx.doi.org/10.1371/journal.pone.0280316 Text en © 2023 Sun et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sun, Hanxi
Plawinski, Jason
Subramaniam, Sajanth
Jamaludin, Amir
Kadir, Timor
Readie, Aimee
Ligozio, Gregory
Ohlssen, David
Baillie, Mark
Coroller, Thibaud
A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs)
title A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs)
title_full A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs)
title_fullStr A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs)
title_full_unstemmed A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs)
title_short A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs)
title_sort deep learning approach to private data sharing of medical images using conditional generative adversarial networks (gans)
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10325103/
https://www.ncbi.nlm.nih.gov/pubmed/37410795
http://dx.doi.org/10.1371/journal.pone.0280316
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