LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression

arXiv:2404.07091v1 Announce Type: new
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 …

abstract arxiv augmentation continuous cs.ai cs.lg data differential disease framework head information node novel ordinary representation representation learning self-supervised learning space ssl supervised learning temporal through type work