# TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes

arXiv: https://arxiv.org/abs/2206.15282 · HTML: https://mlsanity.com/paper/2206.15282/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Builds on (most cited first, 12 of 12)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [Understanding Dimensional Collapse in Contrastive Self-supervised Learning](https://mlsanity.com/paper/2110.09348/) (2021)
- [Big Self-Supervised Models Advance Medical Image Classification](https://mlsanity.com/paper/2101.05224/) (2021)
- [Whitening for Self-Supervised Representation Learning](https://mlsanity.com/paper/2007.06346/) (2020)
- [USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning](https://mlsanity.com/paper/2011.13066/) (2020)
- [Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning](https://mlsanity.com/paper/1910.09420/) (2019)
- [Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations](https://mlsanity.com/paper/2103.04167/) (2021)

## Built on by (most cited first, 12 of 12)

- [Towards multi-modal anatomical landmark detection for ultrasound-guided brain tumor resection with contrastive learning](https://mlsanity.com/paper/2307.14523/) (2023)
- [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)
- [Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data](https://mlsanity.com/paper/2502.07836/) (2025)
- [Masked Image Modelling for retinal OCT understanding](https://mlsanity.com/paper/2405.14788/) (2024)
- [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)
- [Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT](https://mlsanity.com/paper/2304.08439/) (2023)
- [Pretrained Deep 2.5D Models for Efficient Predictive Modeling from Retinal OCT](https://mlsanity.com/paper/2307.13865/) (2023)
- [Data-driven techniques for translational neuroscience and personalized neuro-health](https://mlsanity.com/paper/2608.13749/) (2026)

## Abstract (arXiv)

Recent contrastive learning methods achieved state-of-the-art in low label regimes. However, the training requires large batch sizes and heavy augmentations to create multiple views of an image. With non-contrastive methods, the negatives are implicitly incorporated in the loss, allowing different images and modalities as pairs. Although the meta-information (i.e., age, sex) in medical imaging is abundant, the annotations are noisy and prone to class imbalance. In this work, we exploited already existing temporal information (different visits from a patient) in a longitudinal optical coherence tomography (OCT) dataset using temporally informed non-contrastive loss (TINC) without increasing complexity and need for negative pairs. Moreover, our novel pair-forming scheme can avoid heavy augmentations and implicitly incorporates the temporal information in the pairs. Finally, these representations learned from the pretraining are more successful in predicting disease progression where the temporal information is crucial for the downstream task. More specifically, our model outperforms existing models in predicting the risk of conversion within a time frame from intermediate age-related macular degeneration (AMD) to the late wet-AMD stage.