Self-supervised learning (SSL) has emerged as a powerful technique for
improving the efficiency and effectiveness of deep learning models. Contrastive
methods are a prominent family of SSL that extract similar representations of
two augmented views of an image while pushing away others in the representation
space as negatives. However, the state-of-the-art contrastive methods require
large batch sizes and augmentations designed for natural images that are
impractical for 3D medical images. To address these limitations, we propose a
new longitudinal SSL method, 3DTINC, based on non-contrastive learning. It is
designed to learn perturbation-invariant features for 3D optical coherence
tomography (OCT) volumes, using augmentations specifically designed for OCT. We
introduce a new non-contrastive similarity loss term that learns temporal
information implicitly from intra-patient scans acquired at different times.
Our experiments show that this temporal information is crucial for predicting
progression of retinal diseases, such as age-related macular degeneration
(AMD). After pretraining with 3DTINC, we evaluated the learned representations
and the prognostic models on two large-scale longitudinal datasets of retinal
OCTs where we predict the conversion to wet-AMD within a six months interval.
Our results demonstrate that each component of our contributions is crucial for
learning meaningful representations useful in predicting disease progression
from longitudinal volumetric scans.

提出一种基于非对比学习的新的纵向自监督学习方法 3DTINC，利用特定于 OCT 的数据增强来学习 3D 光学相干断层扫描（OCT）体积的扰动不变特征，实验证明使用这种方法学习到的时间信息对于预测视网膜疾病（如年龄相关性黄斑变性（AMD））的进展至关重要，该方法对于从纵向体积扫描中预测疾病进展有着实际意义。