A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs
This addresses privacy concerns in biomedical data sharing for healthcare research, but it is incremental as it applies an existing GAN method to a specific medical dataset.
The paper tackles the problem of sharing sensitive medical images by generating synthetic MRI datasets using a conditional GAN, achieving high fidelity and privacy preservation for vertebral unit images from a clinical study.
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 clinical study. We apply an Auxiliary Classifier GAN to generate synthetic MRIs of vertebral units. 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 along three key metrics: image fidelity, sample diversity and dataset privacy.