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

arXiv: https://arxiv.org/abs/2404.07091 · HTML: https://mlsanity.com/paper/2404.07091/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 24 references that are in the map.
- First posted on arXiv 10 Apr 2024.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series](https://mlsanity.com/paper/1905.12374/) (2019)
- [Latent ODEs for Irregularly-Sampled Time Series](https://mlsanity.com/paper/1907.03907/) (2019)
- [Learning Long-Term Dependencies in Irregularly-Sampled Time Series](https://mlsanity.com/paper/2006.04418/) (2020)
- [STEER: Simple Temporal Regularization For Neural ODEs](https://mlsanity.com/paper/2006.10711/) (2020)
- [Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression](https://mlsanity.com/paper/2106.02875/) (2021)
- [Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning](https://mlsanity.com/paper/1910.09420/) (2019)
- [Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis](https://mlsanity.com/paper/2206.04281/) (2022)
- [Learning Spatio-Temporal Model of Disease Progression with NeuralODEs from Longitudinal Volumetric Data](https://mlsanity.com/paper/2211.04234/) (2022)
- [Metadata-enhanced contrastive learning from retinal optical coherence tomography images](https://mlsanity.com/paper/2208.02529/) (2022)
- [Predicting Risk of Developing Diabetic Retinopathy using Deep Learning](https://mlsanity.com/paper/2008.04370/) (2020)
- [LMT: Longitudinal Mixing Training, a Framework to Predict Disease Progression from a Single Image](https://mlsanity.com/paper/2310.10420/) (2023)
- [Longitudinal Self-supervised Learning Using Neural Ordinary Differential Equation](https://mlsanity.com/paper/2310.10431/) (2023)
- [LSOR: Longitudinally-Consistent Self-Organized Representation Learning](https://mlsanity.com/paper/2310.00213/) (2023)
- [Incorporating NODE with Pre-trained Neural Differential Operator for Learning Dynamics](https://mlsanity.com/paper/2106.04166/) (2021)

## Built on by (most cited first, 5 of 5)

- [Temporal Flow Matching for Learning Spatio-Temporal Trajectories in 4D Longitudinal Medical Imaging](https://mlsanity.com/paper/2508.21580/) (2025)
- [Alzheimers Disease Progression Prediction Based on Manifold Mapping of Irregularly Sampled Longitudinal Data](https://mlsanity.com/paper/2511.20154/) (2025)
- [Chronological Contrastive Learning: Few-Shot Progression Assessment in Irreversible Diseases](https://mlsanity.com/paper/2603.21935/) (2026)
- [Constrained latent state modeling: A unifying perspective on representation learning under competing constraints](https://mlsanity.com/paper/2605.15995/) (2026)
- [DiffSight-Former: Modeling Structural Differences and Temporal Dynamics for Glaucoma Progression Prediction](https://mlsanity.com/paper/2606.09140/) (2026)

## Abstract (arXiv)

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.