Deep learning has seen rapid growth in recent years and achieved state-of-the-art performance in a wide range of applications. However, training models typically requires expensive and time-consuming collection of large quantities of labeled data. This is particularly true within the scope of medical imaging analysis (MIA), where data are limited and labels are expensive to be acquired. Thus, label-efficient deep learning methods are developed to make comprehensive use of the labeled data as well as the abundance of unlabeled and weak-labeled data. In this survey, we extensively investigated over 300 recent papers to provide a comprehensive overview of recent progress on label-efficient learning strategies in MIA. We first present the background of label-efficient learning and categorize the approaches into different schemes. Next, we examine the current state-of-the-art methods in detail through each scheme. Specifically, we provide an in-depth investigation, covering not only canonical semi-supervised, self-supervised, and multi-instance learning schemes, but also recently emerged active and annotation-efficient learning strategies. Moreover, as a comprehensive contribution to the field, this survey not only elucidates the commonalities and unique features of the surveyed methods but also presents a detailed analysis of the current challenges in the field and suggests potential avenues for future research.

翻译：深度学习近年来发展迅速，在众多应用领域已取得最先进性能。然而，训练模型通常需要大量标注数据的昂贵且耗时采集过程，这在医学图像分析（MIA）领域尤为突出——该领域数据稀缺且标注成本高昂。为此，研究者开发了标签高效深度学习方法，以综合利用有限的标注数据以及丰富的无标注和弱标注数据。本综述广泛调研了300余篇近期论文，全面梳理了MIA中标签高效学习策略的最新进展。我们首先阐释标签高效学习的背景，并将各类方法归纳为不同框架。随后，通过各框架对当前最先进方法进行详细剖析，具体涵盖经典的半监督学习、自监督学习、多实例学习框架，以及近年兴起的主动学习和标注高效学习策略。作为对该领域的综合贡献，本综述不仅揭示了所调研方法的共性与独特特征，还深入分析了当前领域面临的主要挑战，并提出了潜在的研究方向。