Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China.
Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China; Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Comput Biol Med. 2024 Nov;182:109206. doi: 10.1016/j.compbiomed.2024.109206. Epub 2024 Sep 26.
In the dynamic realm of practical clinical scenarios, Continual Learning (CL) has gained increasing interest in medical image analysis due to its potential to address major challenges associated with data privacy, model adaptability, memory inefficiency, prediction robustness and detection accuracy. In general, the primary challenge in adapting and advancing CL remains catastrophic forgetting. Beyond this challenge, recent years have witnessed a growing body of work that expands our comprehension and application of continual learning in the medical domain, highlighting its practical significance and intricacy. In this paper, we present an in-depth and up-to-date review of the application of CL in medical image analysis. Our discussion delves into the strategies employed to address specific tasks within the medical domain, categorizing existing CL methods into three settings: Task-Incremental Learning, Class-Incremental Learning, and Domain-Incremental Learning. These settings are further subdivided based on representative learning strategies, allowing us to assess their strengths and weaknesses in the context of various medical scenarios. By establishing a correlation between each medical challenge and the corresponding insights provided by CL, we provide a comprehensive understanding of the potential impact of these techniques. To enhance the utility of our review, we provide an overview of the commonly used benchmark medical datasets and evaluation metrics in the field. Through a comprehensive comparison, we discuss promising future directions for the application of CL in medical image analysis. A comprehensive list of studies is being continuously updated at https://github.com/xw1519/Continual-Learning-Medical-Adaptation.
在实际临床场景的动态领域中，连续学习（CL）由于其在解决与数据隐私、模型适应性、内存效率、预测稳健性和检测准确性相关的主要挑战方面的潜力，在医学图像分析中引起了越来越多的关注。一般来说，适应和推进 CL 的主要挑战仍然是灾难性遗忘。除此之外，近年来越来越多的工作扩展了我们对连续学习在医学领域的理解和应用，突出了其实际意义和复杂性。在本文中，我们对 CL 在医学图像分析中的应用进行了深入和最新的综述。我们的讨论深入研究了用于解决医学领域特定任务的策略，将现有的 CL 方法分为三种设置：任务增量学习、类别增量学习和领域增量学习。这些设置根据代表性学习策略进一步细分，使我们能够在各种医学场景下评估它们的优缺点。通过将每个医学挑战与 CL 提供的相应见解联系起来，我们全面了解了这些技术的潜在影响。为了增强我们的综述的实用性，我们提供了该领域中常用的基准医学数据集和评估指标的概述。通过全面比较，我们讨论了 CL 在医学图像分析中的应用的有前途的未来方向。研究的综合列表在 https://github.com/xw1519/Continual-Learning-Medical-Adaptation 上不断更新。
