mlsanityData to 7 Oct 2026

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

arXiv 2404.07091 ↗First posted 10 Apr 2024cs.LG cs.AIcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 19 of its 24 references that are in the map. First posted on arXiv 10 Apr 2024.

Builds on

19 of 24 references.

References · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  3. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  4. 2018Neural Ordinary Differential Equationscited by 3,733
  5. 2016Colorful Image Colorizationcited by 1,113
  6. 2019GRU-ODE-Bayes: Continuous modeling of sporadically-observed time seriescited by 230
  7. 2019Latent ODEs for Irregularly-Sampled Time Seriescited by 182
  8. 2020Learning Long-Term Dependencies in Irregularly-Sampled Time Seriescited by 82
  9. 2020STEER: Simple Temporal Regularization For Neural ODEscited by 58
  10. 2021Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progressioncited by 48
  11. 2019Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learningcited by 21
  12. 2022Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysiscited by 19
7 more
  1. 2022Learning Spatio-Temporal Model of Disease Progression with NeuralODEs from Longitudinal Volumetric Datacited by 19
  2. 2022Metadata-enhanced contrastive learning from retinal optical coherence tomography imagescited by 14
  3. 2020Predicting Risk of Developing Diabetic Retinopathy using Deep Learningcited by 8
  4. 2023LMT: Longitudinal Mixing Training, a Framework to Predict Disease Progression from a Single Imagecited by 3
  5. 2023Longitudinal Self-supervised Learning Using Neural Ordinary Differential Equationcited by 3
  6. 2023LSOR: Longitudinally-Consistent Self-Organized Representation Learningcited by 2
  7. 2021Incorporating NODE with Pre-trained Neural Differential Operator for Learning Dynamicscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Temporal Flow Matching for Learning Spatio-Temporal Trajectories in 4D Longitudinal Medical Imagingcited by 5
  2. 2025Alzheimers Disease Progression Prediction Based on Manifold Mapping of Irregularly Sampled Longitudinal Datacited by 0
  3. 2026Chronological Contrastive Learning: Few-Shot Progression Assessment in Irreversible Diseasescited by 0
  4. 2026Constrained latent state modeling: A unifying perspective on representation learning under competing constraintscited by 0
  5. 2026DiffSight-Former: Modeling Structural Differences and Temporal Dynamics for Glaucoma Progression Predictioncited by 0