Left Atrium Segmentation
5 papers with code • 1 benchmarks • 0 datasets
We design a novel uncertainty-aware scheme to enable the student model to gradually learn from the meaningful and reliable targets by exploiting the uncertainty information.
Such mutual consistency encourages the two decoders to have consistent and low-entropy predictions and enables the model to gradually capture generalized features from these unlabeled challenging regions.
Deep learning has achieved promising segmentation performance on 3D left atrium MR images.
Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data
For the inter-domain learning, a consistency constraint is applied to the LAs modelled by two dual-modelling networks to exploit the complementary knowledge among different data domains.
Based on this, the feature extractor is constrained to encourage the consistency of probability maps generated by classifiers under diversified features.