bibtext: @InProceedings{ Zeg_LaTiM_MICCAI2024,
                   author = { Zeghlache, Rachid and Conze, Pierre-Henri and El Habib Daho, Mostafa and Li, Yihao and Le Boité, Hugo and Tadayoni, Ramin and Massin, Pascale and Cochener, Béatrice and Rezaei, Alireza and Brahim, Ikram and Quellec, Gwenolé and Lamard, Mathieu },
                   title = { { LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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 framework trained through self-supervised learning (SSL) to leverage temporal information in latent space for data augmentation. This approach effectively integrates NODEs with SSL, offering significant performance improvements compared to traditional methods that lack explicit temporal integration. We demonstrate the effectiveness of our strategy for diabetic retinopathy progression prediction using the OPHDIAT database. Compared to the baseline, all NODE architectures achieve statistically significant improvements in area under the ROC curve (AUC) and Kappa metrics, highlighting the efficacy of pre-training with SSL-inspired approaches. Additionally, our framework promotes stable training for NODEs, a commonly encountered challenge in time-aware modeling.
title: LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression
authors: Zeghlache, Rachid Conze, Pierre-Henri El Habib Daho, Mostafa Li, Yihao Le Boité, Hugo Tadayoni, Ramin Massin, Pascale Cochener, Béatrice Rezaei, Alireza Brahim, Ikram Quellec, Gwenolé Lamard, Mathieu