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Coarse to Fine: Iterative Adversarial Neural Cellular Automata for Medical Image Synthesis

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

Large-scale, publicly available datasets have driven advances in deep learning, but privacy and legal restrictions often limit data sharing in medical imaging. Synthetic data generation offers a privacy-friendly alternative to enable the training of high-performance models on health data. While most state-of-the-art generative models produce high-quality images, they remain computationally expensive, which limits their applicability on resource-constrained hardware. We propose StyleGANCA, the first lightweight general-purpose NCA-based generative adversarial network. The architecture integrate

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