  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.
