综述医学影像中少样本、弱标注下的深度学习方法，助你快速掌握前沿进展。
Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey
- 系统梳理2018年以来600篇相关研究，分类总结弱监督学习范式
- 覆盖脑、胸、心脏等多领域图像分类、分割与检测任务
- 适合关注医疗AI数据瓶颈与低资源学习的研究者参考
深度学习在医学影像领域取得显著突破，但其性能高度依赖大规模高质量标注数据。然而，医学专家标注耗时耗力，难以获取。因此，针对标注不完整、不准确或缺失的学习范式日益受到关注。本综述系统分类并回顾了该领域的最新研究，涵盖自2018年以来约600篇重要文献，涉及脑、胸、心脏等多类医学影像的图像分类、分割与检测任务。论文厘清了不同研究间的关联关系，给出了各类学习范式的正式定义，全面总结并解读了多种学习机制与策略，有助于读者把握当前研究脉络与核心思想。同时，还探讨了未来可能面临的挑战。
原文摘要 · Abstract (English)
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.
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