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.

翻译：自监督学习（SSL）已成为提升深度学习模型效率与有效性的强大技术。对比学习作为SSL的重要分支，通过将同一图像两个增强视图的表征拉近，同时将不同视图的表征在表征空间中推远（作为负样本）来学习。然而，现有最先进的对比方法需要大批量训练数据以及为自然图像设计的增强策略，这并不适用于三维医学图像。为解决这些局限，我们提出了一种基于非对比学习的新型纵向SSL方法——3DTINC。该方法通过专门为OCT设计的增强策略，学习三维光学相干断层扫描（OCT）容积数据的扰动不变特征。我们引入了一种新的非对比相似性损失项，能够从同一患者不同时间获取的扫描数据中隐式学习时间信息。实验表明，该时间信息对于预测年龄相关性黄斑变性（AMD）等视网膜疾病进展至关重要。在3DTINC预训练后，我们在两个大规模视网膜OCT纵向数据集上评估了所学表征及预后模型，任务为预测六个月内向湿性AMD的转化。结果表明，我们提出的每个组件对于从纵向容积扫描数据中学习有意义的表征以预测疾病进展均至关重要。