9 citations · 14 across the 15 of their papers we have counts for
15 papers
Conditional Neural Optimal Transport for Predicting Cellular Phenotypes from Molecular Structure
Gauthier Avité, Maxime Sanchez-Renauld, Nicolas Bourriez +1
High-content microscopy enables systematic profiling of cellular responses to chemical perturbations, but the scale of the chemical space makes exhaustive phenotypic characterizati…
Deep Learning for BioImaging: What Are We Really Learning?
Ivan Svatko, Maxime Sanchez, Ihab Bendidi +2
Representation learning has driven major advances in natural image analysis by enabling models to acquire high-level semantic features. In microscopy imaging, however, it remains u…
Spectral Collapse in Diffusion Inversion
Nicolas Bourriez, Alexandre Verine, Auguste Genovesio
Conditional diffusion inversion provides a powerful framework for unpaired image-to-image translation. However, we demonstrate through an extensive analysis that standard determini…
XFACTORS: Disentangled Information Bottleneck via Contrastive Supervision
Alexandre Myara, Nicolas Bourriez, Thomas Boyer +3
Disentangled representation learning aims to map independent factors of variation to independent representation components. On one hand, purely unsupervised approaches have proven…
Multi-marginal temporal Schrödinger Bridge Matching from unpaired data
Thomas Gravier, Thomas Boyer, Auguste Genovesio
Many natural dynamic processes -- such as in vivo cellular differentiation or disease progression -- can only be observed through the lens of static sample snapshots. While challen…
The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results
Qiuyu Chen, Xin Jin, Yue Song +45
This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop a…
DiViD: Disentangled Video Diffusion for Static-Dynamic Factorization
Marzieh Gheisari, Auguste Genovesio
Unsupervised disentanglement of static appearance and dynamic motion in video remains a fundamental challenge, often hindered by information leakage and blurry reconstructions in e…
A Cross Modal Knowledge Distillation & Data Augmentation Recipe for Improving Transcriptomics Representations through Morphological Features
Ihab Bendidi, Yassir El Mesbahi, Alisandra K. Denton +4
Understanding cellular responses to stimuli is crucial for biological discovery and drug development. Transcriptomics provides interpretable, gene-level insights, while microscopy…
DiffEx: Explaining a Classifier with Diffusion Models to Identify Microscopic Cellular Variations
Anis Bourou, Saranga Kingkor Mahanta, Thomas Boyer +2
In recent years, deep learning models have been extensively applied to biological data across various modalities. Discriminative deep learning models have excelled at classifying i…
Revealing Subtle Phenotypes in Small Microscopy Datasets Using Latent Diffusion Models
Anis Bourou, Biel Castaño Segade, Thomas Boyer +2
Identifying subtle phenotypic variations in cellular images is critical for advancing biological research and accelerating drug discovery. These variations are often masked by the…
GANs Conditioning Methods: A Survey
Anis Bourou, Valérie Mezger, Auguste Genovesio
In recent years, Generative Adversarial Networks (GANs) have seen significant advancements, leading to their widespread adoption across various fields. The original GAN architectur…
Reconstructing Interpretable Features in Computational Super-Resolution microscopy via Regularized Latent Search
Marzieh Gheisari, Auguste Genovesio
Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. However, such m…
PhenDiff: Revealing Subtle Phenotypes with Diffusion Models in Real Images
Anis Bourou, Thomas Boyer, Kévin Daupin +4
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 s…
Weakly supervised cross-modal learning in high-content screening
Watkinson Gabriel, Cohen Ethan, Bourriez Nicolas +3
With the surge in available data from various modalities, there is a growing need to bridge the gap between different data types. In this work, we introduce a novel approach to lea…
ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images
Nicolas Bourriez, Ihab Bendidi, Ethan Cohen +4
Unlike color photography images, which are consistently encoded into RGB channels, biological images encompass various modalities, where the type of microscopy and the meaning of e…