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.

我们提出了一种时间等变对比学习方法，通过引入时间诱发的转换来捕捉纵向成像过程中的变化，该方法在大规模纵向数据集上优于现有的等变对比方法，用于预测中间年龄相关性黄斑变性（AMD）转为先进型湿型 AMD 的进展。