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arXiv · 2602.14512

MedVAR: Towards Scalable and Efficient Medical Image Generation via Next-scale Autoregressive Prediction

Zhicheng He·Yunpeng Zhao·Junde Wu·Ziwei Niu·Zijun Li·Bohan Li·Lanfen Lin·Yueming Jin
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Abstract

Medical image generation is pivotal in applications like data augmentation for low-resource clinical tasks and privacy-preserving data sharing. However, developing a scalable generative backbone for medical imaging requires architectural efficiency, sufficient multi-organ data, and principled evaluation, yet current approaches leave these aspects unresolved. Therefore, we introduce MedVAR, the first autoregressive-based foundation model that adopts the next-scale prediction paradigm to enable fast and scale-up-friendly medical image synthesis. MedVAR generates images in a coarse-to-fine manner and produces structured multi-scale representations suitable for downstream use. To support hierarchical generation, we curate a harmonized dataset of around 440,000 CT and MRI images spanning six anatomical regions. Comprehensive experiments across fidelity, diversity, and scalability show that MedVAR achieves state-of-the-art generative performance and offers a promising architectural direction for future medical generative foundation models.

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Zhicheng He, Yunpeng Zhao, Junde Wu, Ziwei Niu, Zijun Li, Bohan Li, Lanfen Lin, Yueming Jin. 2026-02-16. MedVAR: Towards Scalable and Efficient Medical Image Generation via Next-scale Autoregressive Prediction. https://arxiv.org/abs/2602.14512

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