A review of research and development of semi-supervised learning strategies for medical image processing

Shengke Yang
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Abstract

Accurate and robust segmentation of organs or lesions from medical images plays a vital role in many clinical applications such as diagnosis and treatment planning. With the massive increase in labeled data, deep learning has achieved great success in image segmentation. However, for medical images, the acquisition of labeled data is usually expensive because generating accurate annotations requires expertise and time, especially in 3D images. To reduce the cost of labeling, many approaches have been proposed in recent years to develop a high-performance medical image segmentation model to reduce the labeling data. For example, combining user interaction with deep neural networks to interactively perform image segmentation can reduce the labeling effort. Self-supervised learning methods utilize unlabeled data to train the model in a supervised manner, learn the basics and then perform knowledge transfer. Semi-supervised learning frameworks learn directly from a limited amount of labeled data and a large amount of unlabeled data to get high quality segmentation results. Weakly supervised learning approaches learn image segmentation from borders, graffiti, or image-level labels instead of using pixel-level labeling, which reduces the burden of labeling. However, the performance of weakly supervised learning and self-supervised learning is still limited on medical image segmentation tasks, especially on 3D medical images. In addition to this, a small amount of labeled data and a large amount of unlabeled data are more in line with actual clinical scenarios. Therefore, semi-supervised learning strategies become very important in the field of medical image processing.
医学图像处理半监督学习策略的研究与开发综述
从医学图像中准确而稳健地分割器官或病灶,在诊断和治疗计划等许多临床应用中发挥着至关重要的作用。随着标注数据的大量增加,深度学习在图像分割方面取得了巨大成功。然而,对于医学图像来说,获取标注数据的成本通常很高,因为生成准确的注释需要专业知识和时间,尤其是在三维图像中。为了降低标注成本,近年来人们提出了许多方法来开发高性能的医学图像分割模型,以减少标注数据。例如,将用户交互与深度神经网络相结合,以交互方式进行图像分割,可以减少标注工作量。自监督学习方法利用未标记数据,以监督方式训练模型,学习基础知识,然后进行知识转移。半监督学习框架直接从有限的标注数据和大量未标注数据中学习,以获得高质量的分割结果。弱监督学习方法从边界、涂鸦或图像级标签中学习图像分割,而不是使用像素级标签,从而减轻了标签的负担。然而,弱监督学习和自我监督学习在医学图像分割任务上的表现仍然有限,尤其是在三维医学图像上。此外,少量标记数据和大量未标记数据更符合实际临床场景。因此,半监督学习策略在医学图像处理领域变得非常重要。
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