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。少样本学习技术能够缓解数据稀缺问题并增强医学图像分析，尤其是在元学习的辅助下。本系统性综述全面概述了医学影像中的少样本学习研究。我们系统检索了文献，筛选出2018年至2023年间发表的80篇相关论文。我们依据医学任务（如肿瘤分割、疾病分类、图像配准）、研究的解剖结构（如心脏、肺部等）以及采用的元学习方法对文章进行了聚类分析。针对每个聚类，我们考察了论文的分布情况以及现有先进方法所提供的结果。此外，我们识别出所有研究中共享的通用流程框架。综述表明，少样本学习能够在多数医学任务中克服数据稀缺问题，而元学习因其能够以少量标注样本适应新任务，成为实施少样本学习的常用选择。同时，在元学习之外，监督学习和半监督学习作为应对医学影像少样本学习挑战的主要技术手段，也展现出最佳性能。最后，我们观察到主要应用领域集中于心脏、肺部和腹部相关研究。本系统性综述旨在启发后续研究，以推动医学图像分析与患者护理的进步。