Abstract
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.
Abstract (translated)
深度学习近年来取得了迅速增长,并在许多应用领域实现了先进的性能。然而,训练模型通常需要昂贵的、耗时的收集大量标记数据。这在医学影像分析(MIA)等领域尤为如此,数据有限,标记费用昂贵。因此,标记高效的深度学习方法被开发,以全面利用标记数据和大量的未标记和弱标记数据。在这个调查中,我们广泛研究了300多篇最新论文,提供了MIA领域标记高效的学习策略的最新进展的全面概述。我们首先介绍了标记高效的学习的背景,并将方法分为不同的Scheme。接着,我们在每个Scheme上详细研究了当前最先进的方法。具体来说,我们进行了深入的研究,不仅涵盖了传统的半监督、自我监督和多实例学习Scheme,而且还最近推出了活跃的、标记高效的学习策略。此外,作为对该领域的全面贡献,这个调查不仅阐明了调查方法之间的共同点和独特之处,还详细分析了该领域当前面临的挑战,并提出了未来研究的潜在的研究方向。
URL
https://arxiv.org/abs/2303.12484
