医学影像中的持续学习：综述与实践分析
Continual Learning in Medical Imaging: A Survey and Practical Analysis
AI总结：
本文系统综述了医学影像中持续学习的研究进展，涵盖分类、分割和检测等任务，提出分类体系并分析挑战与未来方向，以推动该领域在临床中的实际应用。
摘要：
深度学习在重塑医学影像方面取得了巨大成功，但仍面临诸多阻碍其广泛应用的挑战。灾难性遗忘和持续演变数据流中的分布偏移等问题，加大了研究与实际应用之间的差距。持续学习（Continual Learning）通过使神经网络能够顺序获取新知识而不遗忘先前所学内容，为解决这些障碍带来了希望。在本综述中，我们全面回顾了医学领域中持续学习的最新文献，突出了近期趋势，并指出了实际问题。具体而言，我们调研了医学领域中关于分类、分割、检测及其他任务的持续学习研究。此外，我们为所综述的研究构建了一个分类体系，识别了其中的挑战，并提供了克服这些挑战的见解。我们还批判性地讨论了医学影像中持续学习的现状，包括识别未解决的问题并概述有前景的未来方向。我们希望本综述能为研究人员提供该领域发展的有用概览，并进一步激发学术界的研究兴趣。为了跟上该领域的快速发展，我们计划定期在 https://github.com/BioMedIA-MBZUAI/awesome-cl-in-medical 更新仓库中的最新相关论文。
英文摘要：
Deep Learning has shown great success in reshaping medical imaging, yet it faces numerous challenges hindering widespread application. Issues like catastrophic forgetting and distribution shifts in the continuously evolving data stream increase the gap between research and applications. Continual Learning offers promise in addressing these hurdles by enabling the sequential acquisition of new knowledge without forgetting previous learnings in neural networks. In this survey, we comprehensively review the recent literature on continual learning in the medical domain, highlight recent trends, and point out the practical issues. Specifically, we survey the continual learning studies on classification, segmentation, detection, and other tasks in the medical domain. Furthermore, we develop a taxonomy for the reviewed studies, identify the challenges, and provide insights to overcome them. We also critically discuss the current state of continual learning in medical imaging, including identifying open problems and outlining promising future directions. We hope this survey will provide researchers with a useful overview of the developments in the field and will further increase interest in the community. To keep up with the fast-paced advancements in this field, we plan to routinely update the repository with the latest relevant papers at https://github.com/BioMedIA-MBZUAI/awesome-cl-in-medical .
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。
