Left Atrium Segmentation

7 papers with code • 1 benchmarks • 0 datasets

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Most implemented papers

Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

yulequan/UA-MT 16 Jul 2019

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.

Semi-supervised Left Atrium Segmentation with Mutual Consistency Training

HiLab-git/DTC 4 Mar 2021

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.

MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels

moucheng2017/mismatchssl 23 Oct 2021

The state-of-the-art SSL methods in image classification utilise consistency regularisation to learn unlabelled predictions which are invariant to input level perturbations.

A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging

bragancas/vnet_pytorch-atriaseg2018 26 Apr 2020

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.

Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation

jacobzhaoziyuan/HCR-MT 21 May 2021

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

Heye-SYSU/AHDC 17 Sep 2021

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.

Parameter Decoupling Strategy for Semi-supervised 3D Left Atrium Segmentation

bx0903/pdc 20 Sep 2021

Based on this, the feature extractor is constrained to encourage the consistency of probability maps generated by classifiers under diversified features.