Assessing Privacy Leakage in Synthetic 3-D PET Imaging using Transversal GAN. (arXiv:2206.06448v1 [eess.IV])

Training computer-vision related algorithms on medical images for disease
diagnosis or image segmentation is difficult in large part due to privacy
concerns. For this reason, generative image models are highly sought after to
facilitate data sharing. However, 3-D generative models are understudied, and
investigation of their privacy leakage is needed. We introduce our 3-D
generative model, Transversal GAN (TrGAN), using head & neck PET images which
are conditioned on tumour masks as a case study. We define quantitative
measures of …

3-d arxiv gan imaging privacy