bibtext: @InProceedings{ Bou_PhenDiff_MICCAI2024,
                   author = { Bourou, Anis and Boyer, Thomas and Gheisari, Marzieh and Daupin, Kévin and Dubreuil, Véronique and De Thonel, Aurélie and Mezger, Valérie and Genovesio, Auguste },
                   title = { { PhenDiff: Revealing Subtle Phenotypes with Diffusion Models in Real Images } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15003 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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.
title: PhenDiff: Revealing Subtle Phenotypes with Diffusion Models in Real Images
authors: Bourou, Anis Boyer, Thomas Gheisari, Marzieh Daupin, Kévin Dubreuil, Véronique De Thonel, Aurélie Mezger, Valérie Genovesio, Auguste