Auguste Genovesio

Conditional Neural Optimal Transport for Predicting Cellular Phenotypes from Molecular Structure

Gauthier Avité, Maxime Sanchez-Renauld, Nicolas Bourriez, Auguste Genovesio

High-content microscopy enables systematic profiling of cellular responses to chemical perturbations, but the scale of the chemical space makes exhaustive phenotypic characterization experimentally infeasible. This motivates computational models that can predict image-derived phenotypes without acquiring the corresponding treated cells. We formulate molecule-induced …

Detection of malaria infection from parasite-free blood smears

Nicolas Bourriez, Saranga Kingkor Mahanta, Ivan Svatko, Eleanor Lacassagne et autres

Malaria affects almost 263 million people worldwide, most of whom live in sub-Saharan countries. In a strategy to reduce malaria-related mortality and limit transmission, diagnosis in endemic areas needs to be immediately available on the field, easy to perform and cheap. Therefore, …

fr, Bénin (code pays fourni par la source)

Static2Dynamic: Reconstructing videos of unobservable cellular, developmental, and disease processes

T. Boyer, Elaine Del Nery, Nathalie Spassky, Auguste Genovesio

Abstract A fundamental limitation in biology is that many of its most important processes unfold as visual dynamics that cannot be directly observed. Development, tissue remodeling, and disease progression often occur deep in living organisms, over extended timescales, and at cellular resolution …

in, fr (code pays fourni par la source)

Deep Learning for BioImaging: What Are We Really Learning?

Ivan Svatko, Maxime Sanchez, Ihab Bendidi, Gilles Cottrell et autres

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 unclear what current representation learning methods really learn. In this work, we conduct a systematic study of representation …

Deep Learning for BioImaging: What Are We Really Learning?

Ivan Svatko, Maxime Sanchez, Ihab Bendidi, Gilles Cottrell et autres

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 unclear what current representation learning methods really learn. In this work, we conduct a systematic study of representation …

Author response: In vivo autofluorescence lifetime imaging of spatial metabolic heterogeneities and learning-induced changes in the Drosophila mushroom body

Philémon Roussel, Mingyi Zhou, Chiara Stringari, Thomas Préat et autres

fr, Maroc (code pays fourni par la source)

In vivo autofluorescence lifetime imaging of spatial metabolic heterogeneities and learning-induced changes in the Drosophila mushroom body

Philémon Roussel, Mingyi Zhou, Chiara Stringari, Thomas Préat et autres

Neuronal energy regulation is increasingly recognized as a critical factor underlying brain functions and their pathological alterations, yet the metabolic dynamics that accompany cognitive processes remain poorly understood. As a label-free and minimally invasive technique, fluorescence lifetime imaging (FLIM) of coenzymes NADH …

fr (code pays fourni par la source)

Spectral Collapse in Diffusion Inversion

Nicolas Bourriez, Alexandre Vérine, Auguste Genovesio

Conditional diffusion inversion provides a powerful framework for unpaired image-to-image translation. However, we demonstrate through an extensive analysis that standard deterministic inversion (e.g. DDIM) fails when the source domain is spectrally sparse compared to the target domain (e.g., super-resolution, sketch-to-image). In these …

Spectral Collapse in Diffusion Inversion

Nicolas Bourriez, Alexandre Vérine, Auguste Genovesio

Conditional diffusion inversion provides a powerful framework for unpaired image-to-image translation. However, we demonstrate through an extensive analysis that standard deterministic inversion (e.g. DDIM) fails when the source domain is spectrally sparse compared to the target domain (e.g., super-resolution, sketch-to-image). In these …

XFACTORS: Disentangled Information Bottleneck via Contrastive Supervision

Alexandre Myara, Nicolas Bourriez, Thomas Boye, Thomas Lemercier et autres

Disentangled representation learning aims to map independent factors of variation to independent representation components. On one hand, purely unsupervised approaches have proven successful on fully disentangled synthetic data, but fail to recover semantic factors from real data without strong inductive biases. On …

XFACTORS: Disentangled Information Bottleneck via Contrastive Supervision

Alexandre Myara, Nicolas Bourriez, Thomas Boye, Thomas Lemercier et autres

Disentangled representation learning aims to map independent factors of variation to independent representation components. On one hand, purely unsupervised approaches have proven successful on fully disentangled synthetic data, but fail to recover semantic factors from real data without strong inductive biases. On …

In vivo autofluorescence lifetime imaging of spatial metabolic heterogeneities and learning-induced changes in the Drosophila mushroom body

Philémon Roussel, Mingyi Zhou, Chiara Stringari, Thomas Préat et autres

Neuronal energy regulation is increasingly recognized as a critical factor underlying brain functions and their pathological alterations, yet the metabolic dynamics that accompany cognitive processes remain poorly understood. As a label-free and minimally invasive technique, fluorescence lifetime imaging (FLIM) of coenzymes NADH …

fr (code pays fourni par la source)