用轻量级神经元自动机生成医学图像，隐私安全且高效。
Coarse to Fine: Iterative Adversarial Neural Cellular Automata for Medical Image Synthesis
- 基于多尺度神经元自动机，通过迭代局部交互生成图像
- 仅617k参数即达最优FID和KID分数，效率远超基线
- 适合资源受限设备，可生成支持分类训练的高质量图像
大规模公开数据集推动了深度学习的发展，但医疗影像中的隐私与法律限制常阻碍数据共享。合成数据生成为健康数据训练高性能模型提供了隐私友好替代方案。尽管现有先进生成模型可产出高质量图像，其计算开销大，难以在资源受限硬件上应用。本文提出StyleGANCA，首个轻量级通用的基于神经元自动机的生成对抗网络。该架构融合类StyleGAN的映射网络与自适应风格调制，嵌入多尺度神经元自动机合成流程，通过迭代局部交互实现隐空间控制的图像生成。我们在BloodMNIST和PathMNIST上与对抗式、变分式、扩散模型及NCA基线对比。实验表明，StyleGANCA以更少参数达到竞争性图像质量，在PathMNIST上仅用617k参数即取得最佳FID与KID分数。下游实验显示生成图像保留类别特异性信息，有效支持多分类器训练。代码已开源：https://github.com/MECLabTUDA/StyleGANCA
原文摘要 · Abstract (English)
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
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