Longitudinal studies, where a series of images from the same set of individuals are acquired at different time-points, represent a popular technique for studying and characterizing temporal dynamics in biomedical applications. The classical approach for longitudinal comparison involves normalizing for nuisance variations, such as image orientation or contrast differences, via pre-processing. Statistical analysis is, in turn, conducted to detect changes of interest, either at the individual or population level. This classical approach can suffer from pre-processing issues and limitations of the statistical modeling. For example, normalizing for nuisance variation might be hard in settings where there are a lot of idiosyncratic changes. In this paper, we present a simple machine learning-based approach that can alleviate these issues. In our approach, we train a deep learning model (called PaIRNet, for Pairwise Image Ranking Network) to compare pairs of longitudinal images, with or without supervision. In the self-supervised setup, for instance, the model is trained to temporally order the images, which requires learning to recognize time-irreversible changes. Our results from four datasets demonstrate that PaIRNet can be very effective in localizing and quantifying meaningful longitudinal changes while discounting nuisance variation. Our code is available at \url{https://github.com/heejong-kim/learning-to-compare-longitudinal-images.git}

翻译：纵向研究通过在不同时间点获取同一组个体的系列图像，是生物医学应用中研究及表征时间动态的常用技术。经典的纵向比较方法通常通过预处理来归一化干扰性变化（如图像方向或对比度差异），进而进行统计分析以检测个体或群体水平上的兴趣变化。这种经典方法可能因预处理问题及统计建模的局限性而受到影响。例如，在存在大量特异性变化的情况下，归一化干扰性变化可能较为困难。本文提出一种基于机器学习的简单方法以缓解这些问题。该方法训练一个深度学习模型（称为PaIRNet，即成对图像排序网络）来比较成对纵向图像，可支持有监督或无监督学习。例如，在自监督设置中，模型被训练为对图像进行时间排序，这要求学习识别不可逆的时间变化。我们在四个数据集上的实验结果表明，PaIRNet能够在有效定位和量化有意义的纵向变化的同时，忽略干扰性变化。我们的代码发布于\url{https://github.com/heejong-kim/learning-to-compare-longitudinal-images.git}