10 citations · 49 across the 12 of their papers we have counts for
12 papers
Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network
Yang Nan, Peng Tang, Guyue Zhang +5
Tissue segmentation is the mainstay of pathological examination, whereas the manual delineation is unduly burdensome. To assist this time-consuming and subjective manual step, rese…
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…
Discriminative Consistent Domain Generation for Semi-supervised Learning
Jun Chen, Heye Zhang, Yanping Zhang +6
Deep learning based task systems normally rely on a large amount of manually labeled training data, which is expensive to obtain and subject to operator variations. Moreover, it do…
FIRE: Unsupervised bi-directional inter-modality registration using deep networks
Chengjia Wang, Giorgos Papanastasiou, Agisilaos Chartsias +3
Inter-modality image registration is an critical preprocessing step for many applications within the routine clinical pathway. This paper presents an unsupervised deep inter-modali…
Direct Quantification for Coronary Artery Stenosis Using Multiview Learning
Dong Zhang, Guang Yang, Shu Zhao +3
The quantification of the coronary artery stenosis is of significant clinical importance in coronary artery disease diagnosis and intervention treatment. It aims to quantify the mo…
Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation
Jun Chen, Guang Yang, Zhifan Gao +9
Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients,…
Direct detection of pixel-level myocardial infarction areas via a deep-learning algorithm
Chenchu Xu, Lei Xu, Zhifan Gao +7
Accurate detection of the myocardial infarction (MI) area is crucial for early diagnosis planning and follow-up management. In this study, we propose an end-to-end deep-learning al…