Abstract
Deep learning has achieved significant breakthroughs in medical imaging, but these advancements are often dependent on large, well-annotated datasets. However, obtaining such datasets poses a significant challenge, as it requires time-consuming and labor-intensive annotations from medical experts. Consequently, there is growing interest in learning paradigms such as incomplete, inexact, and absent supervision, which are designed to operate under limited, inexact, or missing labels. This survey categorizes and reviews the evolving research in these areas, analyzing around 600 notable contributions since 2018. It covers tasks such as image classification, segmentation, and detection across various medical application areas, including but not limited to brain, chest, and cardiac imaging. We attempt to establish the relationships among existing research studies in related areas. We provide formal definitions of different learning paradigms and offer a comprehensive summary and interpretation of various learning mechanisms and strategies, aiding readers in better understanding the current research landscape and ideas. We also discuss potential future research challenges.
Abstract (translated)
深度学习在医学影像领域取得了显著突破，但这些进展往往依赖于大规模且标注良好的数据集。然而，获取这样的数据集面临着巨大挑战，因为需要耗费大量时间和劳动力进行来自医疗专家的详细注释工作。因此，人们对不完整监督、近似监督和无监督等学习范式越来越感兴趣，这些范式旨在应对有限、不准确或缺失标签的情况。本综述对2018年以来约600项相关研究进行了分类与回顾，涵盖了图像分类、分割及检测等任务，并扩展到脑部、胸部和心脏成像等多个医学应用领域。我们试图建立相关研究之间的联系并提供各种学习范式的正式定义，全面总结和解释了不同的学习机制和策略，帮助读者更好地理解当前的研究格局和发展趋势。此外，我们也探讨了一些潜在的未来研究挑战。
URL
https://arxiv.org/abs/2504.11588
