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.
Generative adversarial networks: An overview
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.IV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.