4 papers
Masked Omics Modeling for Multimodal Representation Learning across Histopathology and Molecular Profiles
Lucas Robinet, Ahmad Berjaoui, Elizabeth Cohen-Jonathan Moyal
Self-supervised learning (SSL) has driven major advances in computational pathology by enabling the learning of rich representations from histopathology data. Yet, tissue analysis…
Multimodal Masked Autoencoder Pre-training for 3D MRI-Based Brain Tumor Analysis with Missing Modalities
Lucas Robinet, Ahmad Berjaoui, Elizabeth Cohen-Jonathan Moyal
Multimodal magnetic resonance imaging (MRI) constitutes the first line of investigation for clinicians in the care of brain tumors, providing crucial insights for surgery planning,…
IMITATE: Image Registration with Context for unknown time frame recovery
Ziad Kheil, Lucas Robinet, Laurent Risser +1
In this paper, we formulate a novel image registration formalism dedicated to the estimation of unknown condition-related images, based on two or more known images and their associ…
DRIM: Learning Disentangled Representations from Incomplete Multimodal Healthcare Data
Lucas Robinet, Ahmad Berjaoui, Ziad Kheil +1
Real-life medical data is often multimodal and incomplete, fueling the growing need for advanced deep learning models capable of integrating them efficiently. The use of diverse mo…