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.

翻译：生成建模旨在近似真实数据的统计特性，从而能够合成与原始分布高度相似的新数据。生成对抗网络（GANs）和去噪扩散概率模型（DDPMs）分别从博弈论和热力学中汲取灵感，代表了生成建模领域的重大进展。然而，从生物进化视角探索生成建模的研究仍基本处于空白。本文受生物体从单细胞进化而来的启发，引入了一类称为生成元胞自动机（GeCA）的新型模型。GeCA被评估为一种有效的增强工具，用于两种成像模态下的视网膜疾病分类：眼底成像和光学相干断层扫描（OCT）。在OCT成像背景下，数据稀缺且类别分布固有偏斜，GeCA显著提升了11种不同眼科疾病的分类性能，与常规基线相比，平均F1分数提高了12%。在相似的参数约束下，GeCA的性能超越了结合UNet的扩散方法，也超越了采用基于Transformer的去噪模型的最新变体。代码发布于：https://github.com/xmed-lab/GeCA。