Scalable next-scale autoregression for medical image generation across anatomical regions
MedVAR introduces a next-scale autoregressive framework for synthesizing medical images. It reformulates generation as a hierarchical prediction problem, using VQVAE tokens ordered from coarse to fine resolution. This approach accelerates sampling and improves stability by modeling global structure before local details, achieving state-of-the-art fidelity and scalability.