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 +1 more
3/15/2023
cs.CVcs.LGq-bio.QM

No official code found. Best implementation: crosszamirski/guided-I2I (55 stars, Apache-2.0, last commit 2023-03-17).

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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Code Implementations(1)

crosszamirski/guided-I2ICommunity100%
Apache-2.0

Combining image-to-image and class guided denoising diffusion probabilistic models.

556PythonDec 6, 20223 years agoApache-2.0

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Cite this paper

@article{crosszamirski2023classguided,
  title  = {Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class Labels},
  author = {Jan Oscar Cross-Zamirski and Praveen Anand and Guy Williams and Elizabeth Mouchet and Yinhai Wang and Carola-Bibiane Schönlieb},
  year   = {2023},
  eprint = {2303.08863},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.08863v2}
}

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