  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.
