# Learning Temporally Equivariance for Degenerative Disease Progression in OCT by Predicting Future Representations Source: [Learning Temporally Equivariance for Degenerative Disease Progression in OCT by Predicting Future Representations]() Collection index: [llms.txt]() Data updated: 2026-01-02T08:30:38.688Z Authors: Taha Emre, Arunava Chakravarty, Dmitrii Lachinov, Antoine Rivail, Ursula Schmidt-Erfurth, Hrvoje Bogunovi\\'c Published: 2024-05-15T15:00:46.000Z ## Abstract Contrastive pretraining provides robust representations by ensuring their invariance to different image transformations while simultaneously preventing representational collapse. Equivariant contrastive learning, on the other hand, provides representations sensitive to specific image transformations while remaining invariant to others. By introducing equivariance to time-induced transformations, such as disease-related anatomical changes in longitudinal imaging, the model can effectively capture such changes in the representation space. In this work, we propose a Time-equivariant Contrastive Learning (TC) method. First, an encoder embeds two unlabeled scans from different time points of the same patient into the representation space. Next, a temporal equivariance module is trained to predict the representation of a later visit based on the representation from one of the previous visits and the corresponding time interval with a novel regularization loss term while preserving the invariance property to irrelevant image transformations. On a large longitudinal dataset, our model clearly outperforms existing equivariant contrastive methods in predicting progression from intermediate age-related macular degeneration (AMD) to advanced wet-AMD within a specified time-window. ## 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 HARBOR 6-month window (test)](): wet-AMD conversion prediction; HARBOR 6-month window (test); AUROC: 0.784. - [wet-AMD conversion prediction on HARBOR 12-month window (test)](): wet-AMD conversion prediction; HARBOR 12-month window (test); AUROC: 0.775. - [Geographic Atrophy (GA) conversion prediction on PINNACLE](): Geographic Atrophy (GA) conversion prediction; PINNACLE; AUROC: 0.77. ## Original sources and implementation [Original paper]() [PDF]()