Identifying subtle phenotypic variations in cellular images is critical for advancing biological research and accelerating drug discovery. These variations are often masked by the inherent cellular heterogeneity, making it challenging to distinguish differences between experimental conditions. Recent advancements in deep generative models have demonstrated significant potential for revealing these nuanced phenotypes through image translation, opening new frontiers in cellular and molecular biology as well as the identification of novel biomarkers. Among these generative models, diffusion models stand out for their ability to produce high-quality, realistic images. However, training diffusion models typically requires large datasets and substantial computational resources, both of which can be limited in biological research. In this work, we propose a novel approach that leverages pre-trained latent diffusion models to uncover subtle phenotypic changes. We validate our approach qualitatively and quantitatively on several small datasets of microscopy images. Our findings reveal that our approach enables effective detection of phenotypic variations, capturing both visually apparent and imperceptible differences. Ultimately, our results highlight the promising potential of this approach for phenotype detection, especially in contexts constrained by limited data and computational capacity.

翻译：识别细胞图像中的细微表型变异对于推进生物学研究和加速药物发现至关重要。这些变异常被固有的细胞异质性所掩盖，使得区分不同实验条件间的差异变得困难。深度生成模型的最新进展已显示出通过图像转换揭示这些微妙表型的巨大潜力，为细胞与分子生物学以及新型生物标志物的识别开辟了新前沿。在这些生成模型中，扩散模型因其能够生成高质量、逼真图像的能力而脱颖而出。然而，训练扩散模型通常需要大型数据集和大量计算资源，这两者在生物学研究中都可能受到限制。在本研究中，我们提出了一种新颖方法，利用预训练的潜在扩散模型来揭示细微的表型变化。我们在多个小型显微图像数据集上对我们的方法进行了定性和定量验证。我们的研究结果表明，该方法能够有效检测表型变异，捕捉到视觉上明显和难以察觉的差异。最终，我们的结果突显了该方法在表型检测方面的巨大潜力，尤其是在数据和计算能力受限的情况下。