发表机构
Technical University of Darmstadt; ImFusion GmbH（达姆施塔特工业大学; ImFusion 有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出首个基于NCA的轻量通用生成对抗网络StyleGANCA，在BloodMNIST和PathMNIST上以仅61.7万参数实现有竞争力的图像质量，支持多分类器训练，解决医学影像数据共享的隐私问题
AI 中文摘要
大规模公开数据集推动了深度学习的发展，但隐私和法律限制往往限制医学影像领域的数据共享。合成数据生成提供了一种隐私友好的替代方案，可用于在健康数据上训练高性能模型。尽管大多数最先进的生成模型能生成高质量图像，但它们的计算成本很高，限制了其在资源受限硬件上的适用性。我们提出StyleGANCA，这是首个基于神经细胞自动机（NCA）的轻量通用生成对抗网络。该架构将受StyleGAN启发的映射网络和自适应风格调制集成到多尺度NCA合成过程中，通过迭代局部交互实现潜在控制的图像生成。我们在BloodMNIST和PathMNIST上，针对对抗、变分、扩散及基于NCA的基准模型对StyleGANCA进行评估。实验结果表明，StyleGANCA以远少于基准架构的参数实现了有竞争力的图像质量，仅用61.7万参数就在PathMNIST上取得了最佳的FID和KID分数。此外，下游实验显示，生成的图像保留了类别特定信息，能有效支持多分类器的训练。我们的代码可在以下网址公开获取：this https URL
英文摘要
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
