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 pro-pose 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.

翻译：对比预训练通过确保表征对不同图像变换的不变性，同时防止表征坍缩，从而提供稳健的表征。而等变对比学习则提供对特定图像变换敏感、但对其他变换保持不变的表示。通过引入对时间诱导变换（如纵向影像中与疾病相关的解剖学变化）的等变性，模型能够有效在表征空间中捕获此类变化。本研究提出一种时间等变对比学习（TC）方法。首先，编码器将同一患者不同时间点的两张未标注扫描图像嵌入表征空间；其次，通过新型正则化损失项训练时间等变模块，基于既往某次就诊的表征及对应时间间隔预测后续就诊的表征，同时保持对无关图像变换的不变性。在大型纵向数据集上，我们的模型在预测中间型年龄相关性黄斑变性（AMD）向晚期湿性AMD的特定时间窗内进展方面，显著优于现有等变对比方法。