The rapid evolution of deep learning has significantly advanced the field of medical image analysis. However, despite these achievements, the further enhancement of deep learning models for medical image analysis faces a significant challenge due to the scarcity of large, well-annotated datasets. To address this issue, recent years have witnessed a growing emphasis on the development of data-efficient deep learning methods. This paper conducts a thorough review of data-efficient deep learning methods for medical image analysis. To this end, we categorize these methods based on the level of supervision they rely on, encompassing categories such as no supervision, inexact supervision, incomplete supervision, inaccurate supervision, and only limited supervision. We further divide these categories into finer subcategories. For example, we categorize inexact supervision into multiple instance learning and learning with weak annotations. Similarly, we categorize incomplete supervision into semi-supervised learning, active learning, and domain-adaptive learning and so on. Furthermore, we systematically summarize commonly used datasets for data efficient deep learning in medical image analysis and investigate future research directions to conclude this survey.

翻译：深度学习的快速发展显著推动了医学影像分析领域的进步。然而，尽管取得了这些成就，深度学习模型在医学影像分析中的进一步提升仍面临重大挑战，即大规模、高质量标注数据集的稀缺性。为解决这一问题，近年来高效数据深度学习方法的研究日益受到重视。本文对面向医学影像分析的高效数据深度学习方法进行了系统性综述。为此，我们根据这些方法所依赖的监督程度对其进行分类，涵盖无监督、不精确监督、不完全监督、不准确监督以及仅有限监督等类别。进一步地，我们将这些类别细化为更具体的子类别。例如，将不精确监督细分为多实例学习和弱标注学习；将不完全监督细分为半监督学习、主动学习和领域自适应学习等。此外，我们系统总结了医学影像分析中高效数据深度学习的常用数据集，并探讨了未来研究方向，以此作为本综述的总结。