# TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes Source: [TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes]() Collection index: [llms.txt]() Data updated: 2026-01-01T15:07:27.234Z Authors: Taha Emre, Arunava Chakravarty, Antoine Rivail, Sophie Riedl, Ursula Schmidt-Erfurth, Hrvoje Bogunovi\\'c Published: 2022-06-30T13:42:09.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Wet-AMD Conversion Prediction on wet-AMD conversion prediction longitudinal OCT (test)](): wet-AMD conversion prediction; wet-AMD conversion prediction longitudinal OCT (test); AUROC: 75.6. - [Wet-AMD Conversion Prediction on HARBOR 6-month window (test)](): wet-AMD conversion prediction; HARBOR 6-month window (test); AUROC: 0.729. - [wet-AMD conversion prediction on HARBOR 12-month window (test)](): wet-AMD conversion prediction; HARBOR 12-month window (test); AUROC: 0.688. ## Original sources and implementation [Original paper]() [PDF]()