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,…
Uncovering the Genetic Basis of Glioblastoma Heterogeneity through Multimodal Analysis of Whole Slide Images and RNA Sequencing Data
Ahmad Berjaoui, Louis Roussel, Eduardo Hugo Sanchez +1
Glioblastoma is a highly aggressive form of brain cancer characterized by rapid progression and poor prognosis. Despite advances in treatment, the underlying genetic mechanisms dri…
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…