Search Results for author: Kuanquan Wang

Found 16 papers, 5 papers with code

Position-prior Clustering-based Self-attention Module for Knee Cartilage Segmentation

1 code implementation21 Jun 2022 Dong Liang, Jun Liu, Kuanquan Wang, Gongning Luo, Wei Wang, Shuo Li

The morphological changes in knee cartilage (especially femoral and tibial cartilages) are closely related to the progression of knee osteoarthritis, which is expressed by magnetic resonance (MR) images and assessed on the cartilage segmentation results.

Transformer Network for Significant Stenosis Detection in CCTA of Coronary Arteries

1 code implementation7 Jul 2021 Xinghua Ma, Gongning Luo, Wei Wang, Kuanquan Wang

However, the complexity of coronary artery plaques that cause CAD makes the automatic detection of coronary artery stenosis in Coronary CT angiography (CCTA) a difficult task.

Weakly Supervised Arrhythmia Detection Based on Deep Convolutional Neural Network

no code implementations10 Dec 2020 Yang Liu, Kuanquan Wang, Qince Li, Runnan He, Yongfeng Yuan, Henggui Zhang

The results show that the models achieve beat-level accuracies of 99. 09% in detecting atrial fibrillation, and 99. 13% in detecting morphological arrhythmias, which are comparable to that of fully supervised learning models, demonstrating their effectiveness.

Arrhythmia Detection ECG Classification +1

Multi-step Cascaded Networks for Brain Tumor Segmentation

1 code implementation16 Aug 2019 Xiangyu Li, Gongning Luo, Kuanquan Wang

Automatic brain tumor segmentation method plays an extremely important role in the whole process of brain tumor diagnosis and treatment.

Brain Tumor Segmentation Data Augmentation +1

Multi-Depth Fusion Network for Whole-Heart CT Image Segmentation

no code implementations IEEE Access 2019 Chengqin Ye, Wei Wang, Shanzhuo Zhang, Kuanquan Wang

Obtaining precise whole-heart segmentation from computed tomography (CT) or other imaging techniques is prerequisite to clinically analyze the cardiac status, which plays an important role in the treatment of cardiovascular diseases.

Computed Tomography (CT) Heart Segmentation +1

Focus On What's Important: Self-Attention Model for Human Pose Estimation

no code implementations22 Sep 2018 Guanxiong Sun, Chengqin Ye, Kuanquan Wang

In this work, we proposed a convolutional network architecture combined with the novel attention model.

Pose Estimation Self-Learning

VoxelAtlasGAN: 3D Left Ventricle Segmentation on Echocardiography with Atlas Guided Generation and Voxel-to-voxel Discrimination

no code implementations10 Jun 2018 Suyu Dong, Gongning Luo, Kuanquan Wang, Shaodong Cao, Ashley Mercado, Olga Shmuilovich, Henggui Zhang, Shuo Li

And cGAN advantageously fuses substantial 3D spatial context information from 3D echocardiography by self-learning structured loss; 2) For the first time, it embeds the atlas into an end-to-end optimization framework, which uses 3D LV atlas as a powerful prior knowledge to improve the inference speed, address the lower contrast and the limited annotation problems of 3D echocardiography; 3) It combines traditional discrimination loss and the new proposed consistent constraint, which further improves the generalization of the proposed framework.

Left Ventricle Segmentation Self-Learning

Multi-views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images

1 code implementation9 Apr 2018 Gongning Luo, Suyu Dong, Kuanquan Wang, WangMeng Zuo, Shaodong Cao, Henggui Zhang

Methods: In this paper, we propose a direct volumes prediction method based on the end-to-end deep convolutional neural networks (CNN).

Detecting atrial fibrillation by deep convolutional neural networks

no code implementations18 Feb 2018 Yong Xia, Naren Wulan, Kuanquan Wang, Henggui Zhang

Conclusion The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection.

Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction

no code implementations3 May 2015 Zhaoxin Li, Kuanquan Wang, WangMeng Zuo, Deyu Meng, Lei Zhang

It is much more promising in suppressing noise while preserving sharp features than conventional isotropic mesh smoothing.

Denoising Image Registration

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