Over the last five years, deep generative models have gradually been adopted for various tasks in biological research. Notably, image-to-image translation methods showed to be effective in revealing subtle phenotypic cell variations otherwise invisible to the human eye. Current methods to achieve this goal mainly rely on Generative Adversarial Networks (GANs). However, these models are known to suffer from some shortcomings such as training instability and mode collapse. Furthermore, the lack of robustness to invert a real image into the latent of a trained GAN prevents flexible editing of real images. In this work, we propose PhenDiff, an image-to-image translation method based on conditional diffusion models to identify subtle phenotypes in microscopy images. We evaluate this approach on biological datasets against previous work such as CycleGAN. We show that PhenDiff outperforms this baseline in terms of quality and diversity of the generated images. We then apply this method to display invisible phenotypic changes triggered by a rare neurodevelopmental disorder on microscopy images of organoids. Altogether, we demonstrate that PhenDiff is able to perform high quality biological image-to-image translation allowing to spot subtle phenotype variations on a real image.

翻译：摘要：过去五年间，深度生成模型已逐渐应用于生物研究中的多项任务。值得注意的是，图像到图像的翻译方法被证明能够有效揭示人眼无法察觉的细微细胞表型变化。目前实现这一目标的方法主要依赖生成对抗网络（GANs）。然而，这类模型存在训练不稳定和模式坍塌等缺陷。此外，将真实图像逆向映射至已训练GAN潜在空间时缺乏鲁棒性，限制了真实图像的灵活编辑。本研究提出PhenDiff——一种基于条件扩散模型的图像到图像翻译方法，用于识别显微镜图像中的细微表型。我们在生物数据集上针对CycleGAN等先前工作进行评估，结果表明PhenDiff在生成图像的质量与多样性方面均优于基线方法。进一步将该方法应用于类器官显微镜图像，用以展示由罕见神经发育障碍触发的不可见表型变化。综上，我们证实PhenDiff能够执行高质量生物图像到图像的翻译，从而在真实图像中定位细微的表型变异。