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arXiv · 2303.08863

Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class Labels

Jan Oscar Cross-Zamirski·Praveen Anand·Guy Williams·Elizabeth Mouchet·Yinhai Wang·Carola-Bibiane Schönlieb
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Abstract

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.

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BibTeXRIS

Jan Oscar Cross-Zamirski, Praveen Anand, Guy Williams, Elizabeth Mouchet, Yinhai Wang, Carola-Bibiane Schönlieb. 2023-03-15. Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class Labels. https://arxiv.org/abs/2303.08863

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