论文标题
有限的注释学习：一项有关医学图像细分的深度半监督学习的调查
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation
论文作者
论文摘要
在许多图像引导的临床方法中，医学图像分割是一个基本和关键的步骤。基于深度学习的分割方法的最新成功通常取决于大量标记的数据，这特别困难且昂贵，尤其是在医学成像领域中，只有专家才能提供可靠，准确的注释。半监督学习已成为一种吸引人的策略，并广泛应用于医疗图像分割任务，以训练注释有限的深层模型。在本文中，我们对最近提出的用于医学图像分割的半监督学习方法进行了全面综述，并总结了技术新颖性和经验结果。此外，我们分析和讨论现有方法的局限性和几个未解决的问题。我们希望这篇评论可以激发研究界探索解决这一挑战的解决方案，并进一步促进医学图像细分领域的发展。
Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly difficult and costly to obtain especially in the medical imaging domain where only experts can provide reliable and accurate annotations. Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarized both the technical novelties and empirical results. Furthermore, we analyze and discuss the limitations and several unsolved problems of existing approaches. We hope this review could inspire the research community to explore solutions for this challenge and further promote the developments in medical image segmentation field.
