Deep semi-supervised learning for medical image segmentation: A review

Abstract

Deep learning has recently demonstrated considerable promise for a variety of computer vision tasks. However, in many practical applications, large-scale labeled datasets are not available, which limits the deployment of deep learning. To address this problem, semi-supervised learning has attracted a lot of attention in the computer vision community, especially in the field of medical image analysis. This paper analyzes existing deep semi-supervised medical image segmentation studies and categories them into five main categories (i.e., pseudo-labeling, consistency regularization, GAN-based methods, contrastive learning-based methods, and hybrid methods). Afterward, we empirically analyze several representative methods by conducting experiments on two common datasets. Besides, we also point out several promising directions for future research. In summary, this paper provides a comprehensive introduction to deep semi-supervised medical image segmentation, aiming to provide a reference and comparison of methods for researchers in this field.