The lack of annotated medical images limits the performance of deep learning models, which usually need large-scale labelled datasets. Few-shot learning techniques can reduce data scarcity issues and enhance medical image analysis, especially with meta-learning. This systematic review gives a comprehensive overview of few-shot learning in medical imaging. We searched the literature systematically and selected 80 relevant articles published from 2018 to 2023. We clustered the articles based on medical outcomes, such as tumour segmentation, disease classification, and image registration; anatomical structure investigated (i.e. heart, lung, etc.); and the meta-learning method used. For each cluster, we examined the papers' distributions and the results provided by the state-of-the-art. In addition, we identified a generic pipeline shared among all the studies. The review shows that few-shot learning can overcome data scarcity in most outcomes and that meta-learning is a popular choice to perform few-shot learning because it can adapt to new tasks with few labelled samples. In addition, following meta-learning, supervised learning and semi-supervised learning stand out as the predominant techniques employed to tackle few-shot learning challenges in medical imaging and also best performing. Lastly, we observed that the primary application areas predominantly encompass cardiac, pulmonary, and abdominal domains. This systematic review aims to inspire further research to improve medical image analysis and patient care.

翻译：标注医学图像的缺乏限制了深度学习模型的性能，这些模型通常需要大规模标注数据集。小样本学习技术能够减少数据稀缺问题并增强医学图像分析能力，尤其是在元学习方法中。本系统性综述全面概述了医学影像中的小样本学习。我们系统地检索了文献，并筛选出2018至2023年间发表的80篇相关文章。我们基于医学结局（如肿瘤分割、疾病分类和图像配准）、所研究的解剖结构（如心脏、肺部等）以及所采用的元学习方法对文章进行聚类。针对每个聚类，我们分析了论文的分布情况以及最先进方法所提供的结果。此外，我们还识别出所有研究共有的通用流程。综述表明，小样本学习能够克服大多数结局中的数据稀缺问题，而元学习是执行小样本学习的热门选择，因为它能通过少量标注样本适应新任务。此外，在元学习之后，监督学习和半监督学习成为解决医学影像中小样本学习挑战的主要且表现最佳的技术。最后，我们观察到主要应用领域涵盖心脏、肺部和腹部区域。本系统性综述旨在启发进一步研究，以改进医学图像分析和患者护理。