5 citations · 9 across the 5 of their papers we have counts for
5 papers
Context-Aware Vision Language Foundation Models for Ocular Disease Screening in Retinal Images
Lucie Berger, Mathieu Lamard, Philippe Zhang +6
Foundation models are large-scale versatile systems trained on vast quantities of diverse data to learn generalizable representations. Their adaptability with minimal fine-tuning m…
LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression
Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho +9
This work proposes a novel framework for analyzing disease progression using time-aware neural ordinary differential equations (NODE). We introduce a "time-aware head" in a framewo…
L-MAE: Longitudinal masked auto-encoder with time and severity-aware encoding for diabetic retinopathy progression prediction
Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho +9
Pre-training strategies based on self-supervised learning (SSL) have proven to be effective pretext tasks for many downstream tasks in computer vision. Due to the significant dispa…
Longitudinal Self-supervised Learning Using Neural Ordinary Differential Equation
Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho +8
Longitudinal analysis in medical imaging is crucial to investigate the progressive changes in anatomical structures or disease progression over time. In recent years, a novel class…
LMT: Longitudinal Mixing Training, a Framework to Predict Disease Progression from a Single Image
Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho +8
Longitudinal imaging is able to capture both static anatomical structures and dynamic changes in disease progression toward earlier and better patient-specific pathology management…