# Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning

arXiv: https://arxiv.org/abs/1910.09420 · HTML: https://mlsanity.com/paper/1910.09420/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

## Built on by (most cited first, 22 of 22)

- [How Transferable Are Self-supervised Features in Medical Image Classification Tasks?](https://mlsanity.com/paper/2108.10048/) (2021)
- [OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics](https://mlsanity.com/paper/2209.11195/) (2022)
- [Metadata-enhanced contrastive learning from retinal optical coherence tomography images](https://mlsanity.com/paper/2208.02529/) (2022)
- [TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes](https://mlsanity.com/paper/2206.15282/) (2022)
- [A Review of Predictive and Contrastive Self-supervised Learning for Medical Images](https://mlsanity.com/paper/2302.05043/) (2023)
- [Clinically Labeled Contrastive Learning for OCT Biomarker Classification](https://mlsanity.com/paper/2305.15154/) (2023)
- [LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression](https://mlsanity.com/paper/2404.07091/) (2024)
- [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)
- [3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs](https://mlsanity.com/paper/2312.16980/) (2023)
- [Detection of diabetic retinopathy using longitudinal self-supervised learning](https://mlsanity.com/paper/2209.00915/) (2022)
- [Learning Temporally Equivariance for Degenerative Disease Progression in OCT by Predicting Future Representations](https://mlsanity.com/paper/2405.09404/) (2024)
- [Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCT](https://mlsanity.com/paper/2409.20195/) (2024)
- [Learning Disease State from Noisy Ordinal Disease Progression Labels](https://mlsanity.com/paper/2503.10440/) (2025)
- [Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challenge](https://mlsanity.com/paper/2506.02976/) (2025)
- [CRONOS: Continuous Time Reconstruction for 4D Medical Longitudinal Series](https://mlsanity.com/paper/2512.16577/) (2025)
- [Stochastic Siamese MAE Pretraining for Longitudinal Medical Images](https://mlsanity.com/paper/2512.23441/) (2025)
- [Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT](https://mlsanity.com/paper/2304.08439/) (2023)
- [Gradient based Severity Labeling for Biomarker Classification in OCT](https://mlsanity.com/paper/2602.19907/) (2026)
- [Representation learning from OCT images](https://mlsanity.com/paper/2605.02589/) (2026)
- [SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models](https://mlsanity.com/paper/2606.19888/) (2026)
- [Deep Learning for Longitudinal Medical Imaging: A Scoping Review](https://mlsanity.com/paper/2610.08838/) (2026)

## Abstract (arXiv)

Longitudinal imaging is capable of capturing the static ana-to-mi-cal structures and the dynamic changes of the morphology resulting from aging or disease progression. Self-supervised learning allows to learn new representation from available large unlabelled data without any expert knowledge. We propose a deep learning self-supervised approach to model disease progression from longitudinal retinal optical coherence tomography (OCT). Our self-supervised model takes benefit from a generic time-related task, by learning to estimate the time interval between pairs of scans acquired from the same patient. This task is (i) easy to implement, (ii) allows to use irregularly sampled data, (iii) is tolerant to poor registration, and (iv) does not rely on additional annotations. This novel method learns a representation that focuses on progression specific information only, which can be transferred to other types of longitudinal problems. We transfer the learnt representation to a clinically highly relevant task of predicting the onset of an advanced stage of age-related macular degeneration within a given time interval based on a single OCT scan. The boost in prediction accuracy, in comparison to a network learned from scratch or transferred from traditional tasks, demonstrates that our pretrained self-supervised representation learns a clinically meaningful information.