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 this https URL

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。