Generative modeling seeks to approximate the statistical properties of real data, enabling synthesis of new data that closely resembles the original distribution. Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPMs) represent significant advancements in generative modeling, drawing inspiration from game theory and thermodynamics, respectively. Nevertheless, the exploration of generative modeling through the lens of biological evolution remains largely untapped. In this paper, we introduce a novel family of models termed Generative Cellular Automata (GeCA), inspired by the evolution of an organism from a single cell. GeCAs are evaluated as an effective augmentation tool for retinal disease classification across two imaging modalities: Fundus and Optical Coherence Tomography (OCT). In the context of OCT imaging, where data is scarce and the distribution of classes is inherently skewed, GeCA significantly boosts the performance of 11 different ophthalmological conditions, achieving a 12% increase in the average F1 score compared to conventional baselines. GeCAs outperform both diffusion methods that incorporate UNet or state-of-the art variants with transformer-based denoising models, under similar parameter constraints. Code is available at: https://github.com/xmed-lab/GeCA.

通过模仿生物进化的方式，本研究引入了一种名为Generative Cellular Automata (GeCA)的新模型系列，作为一种有效的补充工具，用于视网膜疾病分类的两种成像模式：Fundus和光学相干断层扫描（OCT）。在OCT成像的背景下，GeCA显著提升了11种不同眼科疾病的性能，相较于传统基线模型，平均F1分数提高了12%。在相似的参数限制下，GeCA优于融合UNet或基于Transformer的去噪模型的扩散方法。

一个有机体始于单个像素细胞：高分辨率图像合成的神经细胞扩散