Quantitative disease severity scoring in medical imaging is costly, time-consuming, and subject to inter-reader variability. At the same time, clinical archives contain far more longitudinal imaging data than expert-annotated severity scores. Existing self-supervised methods typically ignore this chronological structure. We introduce ChronoCon, a contrastive learning approach that replaces label-based ranking losses with rankings derived solely from the visitation order of a patient's longitudinal scans. Under the clinically plausible assumption of monotonic progression in irreversible diseases, the method learns disease-relevant representations without using any expert labels. This generalizes the idea of Rank-N-Contrast from label distances to temporal ordering. Evaluated on rheumatoid arthritis radiographs for severity assessment, the learned representations substantially improve label efficiency. In low-label settings, ChronoCon significantly outperforms a fully supervised baseline initialized from ImageNet weights. In a few-shot learning experiment, fine-tuning ChronoCon on expert scores from only five patients yields an intraclass correlation coefficient of 86% for severity score prediction. These results demonstrate the potential of chronological contrastive learning to exploit routinely available imaging metadata to reduce annotation requirements in the irreversible disease domain. Code is available at https://github.com/cirmuw/ChronoCon.

翻译：医学影像中的定量疾病严重程度评分成本高昂、耗时，且受读者间差异影响。与此同时，临床档案中包含的纵向影像数据远多于专家标注的严重程度评分。现有自监督方法通常忽略这种时间序结构。我们提出ChronoCon，一种对比学习方法，该方法用单纯从患者纵向扫描就诊顺序导出的排序替代基于标签的排序损失。在不可逆疾病单调进展这一临床合理假设下，该方法无需使用任何专家标签即可学习疾病相关表征。这从标签距离推广到时间排序，拓展了Rank-N-Contrast的思想。在类风湿关节炎X光片的严重程度评估中，学习到的表征显著提升了标签效率。在低标签设置下，ChronoCon显著优于基于ImageNet权重初始化的全监督基线。在少样本学习实验中，仅对五名患者的专家评分微调ChronoCon，即可在严重程度评分预测中达到86%的组内相关系数。这些结果表明，时间序对比学习在利用常规可获取的影像元数据减少不可逆疾病领域的标注需求方面具有潜力。代码见https://github.com/cirmuw/ChronoCon。