Image-to-image reconstruction problems with free or inexpensive metadata in the form of class labels appear often in biological and medical image domains. Existing text-guided or style-transfer image-to-image approaches do not translate to datasets where additional information is provided as discrete classes. We introduce and implement a model which combines image-to-image and class-guided denoising diffusion probabilistic models. We train our model on a real-world dataset of microscopy images used for drug discovery, with and without incorporating metadata labels. By exploring the properties of image-to-image diffusion with relevant labels, we show that class-guided image-to-image diffusion can improve the meaningful content of the reconstructed images and outperform the unguided model in useful downstream tasks.

翻译：图像到图像重构问题中常伴随着以类别标签形式存在的免费或低成本元数据，这在生物和医学图像领域尤为常见。现有的文本引导或风格迁移式图像到图像方法无法适用于以离散类别形式提供额外信息的数据集。我们提出并实现了一种融合图像到图像与类别引导去噪扩散概率模型的方案。我们在用于药物发现的真实世界显微图像数据集上训练该模型，并对比了是否包含元数据标签两种情况。通过探究具有相关标签的图像到图像扩散特性，我们证明了类别引导的图像到图像扩散能够提升重建图像中有意义内容的丰富度，并在下游任务中优于无引导模型。