CycleMatch: Cyclic pseudo-labeling distillation in semi-supervised medical image segmentation

Abstract

In this study, we present a semi-supervised medical image segmentation framework called CycleMatch, which aims to tackle the dependency of fully supervised methods on a large amount of labeled data. By integrating a cyclic pseudo-label distillation mechanism with image-level and feature-level perturbations, CycleMatch effectively leverages unlabeled data to enhance model performance and robustness. Experimental results demonstrate that CycleMatch outperforms existing semi-supervised methods across various data annotation ratios, particularly excelling in scenarios with limited labeled data. Additionally, an in-depth analysis of feature perturbation types and parameter choices further validates CycleMatch’s effectiveness and adaptability in handling different medical image datasets. Overall, CycleMatch offers a new solution for medical image segmentation, showcasing the potential for achieving efficient and accurate segmentation even with limited data.