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：近年来，深度学习发展迅速，在众多应用中取得了先进性能。然而，训练模型通常需要昂贵且耗时的大量标注数据收集。这在医学影像分析领域尤为突出，因为数据有限且获取标注成本高昂。为充分利用标注数据以及丰富的无标注和弱标注数据，标签高效深度学习方法应运而生。本综述广泛调研了300余篇近期论文，全面梳理了医学影像分析中标签高效学习策略的最新进展。我们首先介绍了标签高效学习的背景，并将各种方法归入不同框架。随后，我们详细审视了每个框架下的当前最先进方法。具体而言，我们进行了深入探究，不仅涵盖经典的半监督、自监督和多实例学习框架，还涉及近期兴起的主动学习和标注高效学习策略。此外，作为对该领域的综合性贡献，本综述不仅阐明了所调研方法的共性与独特特征，还详细分析了当前领域面临的挑战，并指出了未来研究的潜在方向。