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，并在广泛的应用中取得了最先进的性能。然而，训练模型通常需要昂贵且耗时地收集大量标记数据。在医学图像分析领域中尤其如此，该领域数据有限且标签获取成本高昂。因此，标签高效深度学习方法应运而生，旨在综合利用标记数据以及丰富的未标记和弱标记数据。在本综述中，我们广泛调研了300余篇近期论文，全面概述了医学图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，并将各类方法归纳为不同框架。接着，我们通过每个框架详细审视了当前的最先进方法。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注释高效学习策略。此外，作为对该领域的全面贡献，本综述不仅阐明了所调研方法的共性与独特特征，还对当前领域的挑战进行了详细分析，并提出了未来研究的潜在方向。