3D PET image generation with tumour masks using TGAN

@inproceedings{Bergen20213DPI,
  title={3D PET image generation with tumour masks using TGAN},
  author={Robert V Bergen and Jean-FranƧois Rajotte and Fereshteh Yousefirizi and Ivan S. Klyuzhin and Arman Rahmim and Raymond T. Ng},
  booktitle={Medical Imaging},
  year={2021}
}
Training computer-vision related algorithms on medical images for disease diagnosis or image segmentation is difficult due to the lack of training data, labeled samples, and privacy concerns. For this reason, a robust generative method to create synthetic data is highly sought after. However, most three-dimensional image generators require additional image input or are extremely memory intensive. To address these issues we propose adapting video generation techniques for 3- D image generationā€¦Ā 
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Assessing Privacy Leakage in Synthetic 3-D PET Imaging using Transversal GAN

It is shown that the discriminator of the TrGAN is vulnerable to attack, and that an attacker can identify which samples were used in training with almost perfect accuracy, suggesting that TrGAN generators, but not discriminators, may be used for sharing synthetic 3-D PET data with minimal privacy risk while maintaining good utility and fidelity.

Synthetic data as an enabler for machine learning applications in medicine

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