医学图像分析中的持续学习：近期进展与未来前景的全面综述
Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects
AI总结：
本文综述了医学图像分析中的持续学习技术，涵盖灾难性遗忘、数据漂移及框架关键组件，评估了排练、正则化、架构和混合策略在放射学等子领域的应用，为开发可持续深度模型提供参考。
摘要：
在先进深度学习算法快速发展的推动下，医学图像分析取得了显著进展，近年来甚至超越了人类水平。然而，当推理数据集与模型在一次性训练期间所见的数据略有不同时，模型性能会受到极大影响。这种情况需要使用旧数据和新数据重新启动训练过程，这在计算上代价高昂，不符合人类学习过程，并带来存储限制和隐私问题。或者，持续学习已成为一种关键方法，用于开发统一且可持续的深度模型，以应对非平稳环境中各种应用领域的新类别、新任务以及数据的漂移特性。持续学习技术使模型能够随时间适应并积累知识，这对于在不断发展的数据集和新任务上保持性能至关重要。本文系统性地综述了应用于医学图像分析的最先进持续学习技术。我们对现有研究进行了广泛调查，涵盖灾难性遗忘、数据漂移、稳定性和可塑性要求等主题。此外，还深入讨论了持续学习框架的关键组成部分，如持续学习场景、技术、评估方案和指标。持续学习技术包含多个类别，包括排练、正则化、架构和混合策略。我们评估了持续学习各类别在放射学和病理学等医学子领域的普及度和适用性。
英文摘要：
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...
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
