A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs

• URL: http://arxiv.org/abs/2106.13199v1
• Date: Thu, 24 Jun 2021 17:24:06 GMT
• Title: A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs
• Authors: Hanxi Sun, Jason Plawinski, Sajanth Subramaniam, Amir Jamaludin, Timor Kadir, Aimee Readie, Gregory Ligozio, David Ohlssen, Mark Baillie, Thibaud Coroller
• Abstract summary: We present a method for generating a synthetic dataset based on COSENTYX (secukinumab) Ankylosing Spondylitis clinical study. In this paper, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties of along three key metrics: image fidelity, sample diversity and dataset privacy.
• Score: 1.2099130772175573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sharing data from clinical studies can facilitate innovative data-driven research and ultimately lead to better public health. However, sharing biomedical data can put sensitive personal information at risk. This is usually solved by anonymization, which is a slow and expensive process. An alternative to anonymization is sharing a synthetic dataset that bears a behaviour similar to the real data but preserves privacy. As part of the collaboration between Novartis and the Oxford Big Data Institute, we generate a synthetic dataset based on COSENTYX (secukinumab) Ankylosing Spondylitis (AS) clinical study. We apply an Auxiliary Classifier GAN (ac-GAN) to generate synthetic magnetic resonance images (MRIs) of vertebral units (VUs). The images are conditioned on the VU location (cervical, thoracic and lumbar). In this paper, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties of along three key metrics: image fidelity, sample diversity and dataset privacy.

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