Clinical deployment of chest radiograph classifiers requires models that can be updated as new datasets become available without retraining on previously ob- served data or degrading validated performance. We study, for the first time, a task-incremental continual learning setting for chest radiograph classification, in which heterogeneous chest X-ray datasets arrive sequentially and task identifiers are unavailable at inference. We propose a continual adapter-based routing learning strategy for Chest X-rays (CARL-XRay) that maintains a fixed high-capacity backbone and incrementally allocates lightweight task-specific adapters and classifier heads. A latent task selector operates on task-adapted features and leverages both current and historical context preserved through compact prototypes and feature-level experience replay. This design supports stable task identification and adaptation across sequential updates while avoiding raw-image storage. Experiments on large-scale public chest radiograph datasets demonstrate robust performance retention and reliable task-aware inference under continual dataset ingestion. CARL-XRay outperforms joint training under task-unknown deployment, achieving higher routing accuracy (75.0\% vs.\ 62.5\%), while maintaining competitive diagnostic performance with AUROC of 0.74 in the oracle setting with ground-truth task identity and 0.75 under task-unknown inference, using significantly fewer trainable parameters. Finally, the proposed framework provides a practical alternative to joint training and repeated full retraining in continual clinical deployment.

翻译：胸片分类器的临床部署要求模型能够在新的数据集可用时进行更新，而无需对先前已观测的数据进行重新训练，也不会降低已验证的性能。我们首次研究了胸片分类的任务增量持续学习场景，其中异构的胸部X光数据集按顺序到达，且在推理时任务标识符不可用。我们提出了一种用于胸部X光的基于适配器的路由持续学习策略（CARL-XRay），该策略维持一个固定的高容量骨干网络，并增量地分配轻量级的任务特定适配器和分类器头。一个潜在任务选择器在任务适配的特征上运行，并利用通过紧凑原型和特征级经验回放所保留的当前及历史上下文。这种设计支持在顺序更新过程中实现稳定的任务识别与适配，同时避免原始图像存储。在大规模公开胸片数据集上的实验表明，在持续数据集摄入下，该方法具有鲁棒的性能保持能力和可靠的任务感知推理能力。CARL-XRay在任务未知的部署场景下优于联合训练，实现了更高的路由准确率（75.0% 对 62.5%），同时在诊断性能上保持竞争力：在拥有真实任务标识的预言机设置下AUROC为0.74，在任务未知推理下AUROC为0.75，且使用的可训练参数显著更少。最后，所提出的框架为持续临床部署中的联合训练和重复的完整再训练提供了一个实用的替代方案。