For the past few years, deep generative models have increasingly been used in biological research for a variety of tasks. Recently, they have proven to be valuable for uncovering subtle cell phenotypic differences that are not directly discernible to the human eye. However, current methods employed to achieve this goal mainly rely on Generative Adversarial Networks (GANs). While effective, GANs encompass issues such as training instability and mode collapse, and they do not accurately map images back to the model's latent space, which is necessary to synthesize, manipulate, and thus interpret outputs based on real images. In this work, we introduce PhenDiff: a multi-class conditional method leveraging Diffusion Models (DMs) designed to identify shifts in cellular phenotypes by translating a real image from one condition to another. We qualitatively and quantitatively validate this method on cases where the phenotypic changes are visible or invisible, such as in low concentrations of drug treatments. Overall, PhenDiff represents a valuable tool for identifying cellular variations in real microscopy images. We anticipate that it could facilitate the understanding of diseases and advance drug discovery through the identification of novel biomarkers.

翻译：过去几年，深度生成模型在生物学研究中被越来越多地应用于各种任务。最近，它们已被证明在揭示人眼无法直接辨别的细微细胞表型差异方面具有重要价值。然而，目前用于实现这一目标的方法主要依赖于生成对抗网络（GANs）。尽管有效，但GANs存在训练不稳定和模式崩溃等问题，并且无法准确地将图像映射回模型的潜在空间，而这一步骤对于基于真实图像合成、操作并进而解释输出结果是必要的。在本研究中，我们提出了PhenDiff：一种利用扩散模型（DMs）的多类条件方法，旨在通过将真实图像从一种条件转换到另一种条件来识别细胞表型的变化。我们在表型变化可见或不可见（例如低浓度药物处理）的案例中，对这种方法进行了定性和定量验证。总体而言，PhenDiff代表了一种在真实显微图像中识别细胞变异的宝贵工具。我们预期，它可以通过识别新的生物标志物，促进对疾病的理解并推动药物发现。