  Consistency training has proven to be an advanced semi-supervised framework
and achieved promising results in medical image segmentation tasks through
enforcing an invariance of the predictions over different views of the inputs.
However, with the iterative updating of model parameters, the models would tend
to reach a coupled state and eventually lose the ability to exploit unlabeled
data. To address the issue, we present a novel semi-supervised segmentation
model based on parameter decoupling strategy to encourage consistent
predictions from diverse views. Specifically, we first adopt a two-branch
network to simultaneously produce predictions for each image. During the
training process, we decouple the two prediction branch parameters by quadratic
cosine distance to construct different views in latent space. Based on this,
the feature extractor is constrained to encourage the consistency of
probability maps generated by classifiers under diversified features. In the
overall training process, the parameters of feature extractor and classifiers
are updated alternately by consistency regularization operation and decoupling
operation to gradually improve the generalization performance of the model. Our
method has achieved a competitive result over the state-of-the-art
semi-supervised methods on the Atrial Segmentation Challenge dataset,
demonstrating the effectiveness of our framework. Code is available at
https://github.com/BX0903/PDC.
