A 3D-VQ-GAN combined with a latent neural ODE generates longitudinal IPF CT scans at arbitrary time points, and codebook-frequency biomarkers from the generated scans achieve C-indices of 0.886 (cross-sectional) and 0.959 (longitudinal) versus 0.929 and 1.0 for real scans.
Seeing what a GAN cannot generate
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4D VQ-GAN: Synthesising Medical Scans at Any Time Point for Personalised Disease Progression Modelling of Idiopathic Pulmonary Fibrosis
A 3D-VQ-GAN combined with a latent neural ODE generates longitudinal IPF CT scans at arbitrary time points, and codebook-frequency biomarkers from the generated scans achieve C-indices of 0.886 (cross-sectional) and 0.959 (longitudinal) versus 0.929 and 1.0 for real scans.