10 citations · 26 across the 6 of their papers we have counts for
10 papers
Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data
Jun Chen, Heye Zhang, Raad Mohiaddin +4
Semi-supervised learning provides great significance in left atrium (LA) segmentation model learning with insufficient labelled data. Generalising semi-supervised learning to cross…
JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets
Jun Chen, Guang Yang, Habib Khan +7
Automated and accurate segmentations of left atrium (LA) and atrial scars from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images are in high demand for quantifyi…
Annealing Genetic GAN for Minority Oversampling
Jingyu Hao, Chengjia Wang, Heye Zhang +1
The key to overcome class imbalance problems is to capture the distribution of minority class accurately. Generative Adversarial Networks (GANs) have shown some potentials to tackl…
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness
Yifeng Guo, Chengjia Wang, Heye Zhang +1
The performance of traditional compressive sensing-based MRI (CS-MRI) reconstruction is affected by its slow iterative procedure and noise-induced artefacts. Although many deep lea…
Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention
Guang Yang, Jun Chen, Zhifan Gao +13
Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to str…
Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation
Ming Li, Weiwei Zhang, Guang Yang +5
Multi-view echocardiographic sequences segmentation is crucial for clinical diagnosis. However, this task is challenging due to limited labeled data, huge noise, and large gaps acr…