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

2026-08-28

AI Summary

为解决医疗图像数据共享受限及生成模型计算成本高问题，提出StyleGANCA，通过迭代局部交互生成高质量图像，参数量少且效果好。

Abstract

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 integrates a StyleGAN-inspired mapping network and adaptive style modulation into a multi-scale NCA synthesis process, enabling latent-controlled image generation through iterative local interactions. We evaluate StyleGANCA on BloodMNIST and PathMNIST against adversarial, variational, diffusion, and NCA-based baselines. Experimental results demonstrate that StyleGANCA achieves competitive image quality with substantially fewer parameters than baseline architectures, achieving the best FID and KID scores on PathMNIST with only 617k parameters. Furthermore, downstream experiments show that the generated images preserve class-specific information and effectively support the training of multi-class classifiers. Our code is publicly available at: https://github.com/MECLabTUDA/StyleGANCA

Problem

• medical imaging
• synthetic data generation
• resource-constrained hardware
• computational cost
• privacy

Innovation

• Neural Cellular Automata
• Lightweight GAN
• Iterative Local Interactions
• Latent-Controlled Generation
• Medical Image Synthesis