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。对比学习是SSL的重要分支，通过将同一图像两个增强视图的表示相互拉近，同时推远表示空间中的其他负样本，来提取相似表示。然而，现有最优对比方法需要大批量处理以及针对自然图像设计的增强策略，这在实际三维医学图像中难以实现。为克服这些局限，我们提出了一种基于非对比学习的新型纵向SSL方法——3DTINC。该方法通过专为光学相干断层扫描（OCT）设计的增强策略，学习三维OCT体数据的扰动不变特征。我们引入了一项新的非对比相似性损失项，可隐式地从患者不同时间采集的影像中学习时间信息。实验表明，此类时间信息对预测年龄相关黄斑变性（AMD）等视网膜疾病进展至关重要。采用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习得表示及预后模型，这些数据需预测六个月内转化为湿性AMD的概率。结果表明，本研究的每个贡献模块对从纵向体数据中学习有意义的表示、进而预测疾病进展具有关键作用。