PhenDiff: Revealing Invisible Phenotypes with Conditional Diffusion
  Models
Anis Bourou Thomas Boyer Kévin Daupin Véronique Dubreuil Aurélie De Thonel Valérie Mezger Auguste Genovesio
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.