Medical imaging analysis has witnessed remarkable advancements even surpassing human-level performance in recent years, driven by the rapid development of advanced deep-learning algorithms. However, when the inference dataset slightly differs from what the model has seen during one-time training, the model performance is greatly compromised. The situation requires restarting the training process using both the old and the new data which is computationally costly, does not align with the human learning process, and imposes storage constraints and privacy concerns. Alternatively, continual learning has emerged as a crucial approach for developing unified and sustainable deep models to deal with new classes, tasks, and the drifting nature of data in non-stationary environments for various application areas. Continual learning techniques enable models to adapt and accumulate knowledge over time, which is essential for maintaining performance on evolving datasets and novel tasks. This systematic review paper provides a comprehensive overview of the state-of-the-art in continual learning techniques applied to medical imaging analysis. We present an extensive survey of existing research, covering topics including catastrophic forgetting, data drifts, stability, and plasticity requirements. Further, an in-depth discussion of key components of a continual learning framework such as continual learning scenarios, techniques, evaluation schemes, and metrics is provided. Continual learning techniques encompass various categories, including rehearsal, regularization, architectural, and hybrid strategies. We assess the popularity and applicability of continual learning categories in various medical sub-fields like radiology and histopathology...

翻译：近年来，先进深度学习算法的快速发展推动医学影像分析取得了显著进展，甚至在特定任务中超越了人类水平。然而，当推理数据集与模型一次性训练时所见数据存在微小差异时，模型性能便会大幅下降。这种情况要求同时使用新旧数据重新启动训练过程，这不仅计算成本高昂，不符合人类学习过程，还带来存储约束和隐私问题。相比之下，持续学习已成为构建统一、可持续深度学习模型的关键方法，用于处理各类应用领域中的新类别、新任务以及非平稳环境下的数据漂移特性。持续学习技术使模型能够随时间推移适应并累积知识，这对于在持续演化的数据集和新任务上保持性能至关重要。本系统性综述论文全面概述了应用于医学影像分析的最先进持续学习技术。我们广泛调研了现有研究，涵盖灾难性遗忘、数据漂移、稳定性与可塑性需求等主题。此外，深入讨论了持续学习框架的关键组件，包括持续学习场景、技术、评估方案与指标。持续学习技术涵盖多种类别，包括重放、正则化、架构和混合策略。我们评估了各类持续学习范畴在放射学、组织病理学等医学子领域中的普及程度与适用性。