Semi-supervised learning for medical image segmentation presents a unique challenge of efficiently using limited labeled data while leveraging abundant unlabeled data. Despite advancements, existing methods often do not fully exploit the potential of the unlabeled data for enhancing model robustness and accuracy. In this paper, we introduce CrossMatch, a novel framework that integrates knowledge distillation with dual perturbation strategies-image-level and feature-level-to improve the model's learning from both labeled and unlabeled data. CrossMatch employs multiple encoders and decoders to generate diverse data streams, which undergo self-knowledge distillation to enhance consistency and reliability of predictions across varied perturbations. Our method significantly surpasses other state-of-the-art techniques in standard benchmarks by effectively minimizing the gap between training on labeled and unlabeled data and improving edge accuracy and generalization in medical image segmentation. The efficacy of CrossMatch is demonstrated through extensive experimental validations, showing remarkable performance improvements without increasing computational costs. Code for this implementation is made available at https://github.com/AiEson/CrossMatch.git.

CrossMatch是一种新颖的框架，通过与双扰动策略（图像级和特征级）结合使用知识蒸馏技术，从标记和未标记数据中提高模型的学习能力，通过生成多样化的数据流进行自知识蒸馏，从而在医学图像分割中显著优于其他最先进的技术，在标准基准测试中有效地缩小训练差距，提高边缘精度和泛化性能。

CrossMatch: 强化半监督医学图像分割的扰动策略和知识蒸馏