SymMatch: Symmetric Bi-Scale Matching with Self-Knowledge Distillation in Semi-Supervised Medical Image Segmentation

With the development of medical image segmentation technology, high-quality automatic segmentation methods, particularly within semi-supervised learning frameworks, have become a research hotspot. This study introduces a new semi-supervised medical image segmentation algorithm called SymMatch. The algorithm effectively leverages limited labeled data along with a large amount of unlabeled data through a symmetrical network structure and knowledge distillation techniques. SymMatch applies a spectrum of perturbations, from weak to strong, at both image and feature levels, effectively leveraging the potential of unlabeled data. Additionally, by incorporating a bi-scale distillation loss, the model’s robustness and accuracy in handling complex medical imaging data are further enhanced. Experimental results show that SymMatch demonstrates superior performance across multiple recognized medical imaging datasets (such as ACDC, LA and PanNuke). Notably, even with very limited labeled data, it maintains high segmentation accuracy. These achievements not only advance the development of semi-supervised medical image segmentation technology but also provide new ideas and methods for future research in related technologies. Code is available at https://github.com/AiEson/SymMatch.