Longitudinal analysis in medical imaging is crucial to investigate the progressive changes in anatomical structures or disease progression over time. In recent years, a novel class of algorithms has emerged with the goal of learning disease progression in a self-supervised manner, using either pairs of consecutive images or time series of images. By capturing temporal patterns without external labels or supervision, longitudinal self-supervised learning (LSSL) has become a promising avenue. To better understand this core method, we explore in this paper the LSSL algorithm under different scenarios. The original LSSL is embedded in an auto-encoder (AE) structure. However, conventional self-supervised strategies are usually implemented in a Siamese-like manner. Therefore, (as a first novelty) in this study, we explore the use of Siamese-like LSSL. Another new core framework named neural ordinary differential equation (NODE). NODE is a neural network architecture that learns the dynamics of ordinary differential equations (ODE) through the use of neural networks. Many temporal systems can be described by ODE, including modeling disease progression. We believe that there is an interesting connection to make between LSSL and NODE. This paper aims at providing a better understanding of those core algorithms for learning the disease progression with the mentioned change. In our different experiments, we employ a longitudinal dataset, named OPHDIAT, targeting diabetic retinopathy (DR) follow-up. Our results demonstrate the application of LSSL without including a reconstruction term, as well as the potential of incorporating NODE in conjunction with LSSL.

翻译：医学影像中的纵向分析对于研究解剖结构的渐进变化或疾病随时间的发展至关重要。近年来，一类新颖的算法应运而生，旨在以自监督方式学习疾病进展，这些算法利用连续图像对或图像时间序列进行训练。通过捕捉时间模式而无需外部标签或监督，纵向自监督学习已成为一条有前景的研究路径。为深入理解这一核心方法，本文在不同场景下探讨了LSSL算法。原始LSSL嵌入自编码器结构中，然而传统自监督策略通常以孪生网络方式实现。因此，作为本文的首个创新点，我们探索了类孪生LSSL的应用。另一个核心新框架是神经常微分方程，它是一种通过神经网络学习常微分方程动态过程的架构。许多时间系统，包括疾病进展建模，均可用常微分方程描述。我们相信LSSL与NODE之间存在有趣的关联。本文旨在结合上述改进，加深对这类疾病进展学习核心算法的理解。在不同实验中，我们使用了针对糖尿病视网膜病变随访的纵向数据集OPHDIAT。研究结果展示了无需重建项的LSSL应用，以及将NODE与LSSL结合的潜在价值。