1.2k citations · 7.7k across the 159 of their papers we have counts for
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PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation
Qi Dou, Cheng Ouyang, Cheng Chen +4
Deep convolutional networks have demonstrated the state-of-the-art performance on various medical image computing tasks. Leveraging images from different modalities for the same an…
Combating Uncertainty with Novel Losses for Automatic Left Atrium Segmentation
Xin Yang, Na Wang, Yi Wang +4
Segmenting left atrium in MR volume holds great potentials in promoting the treatment of atrial fibrillation. However, the varying anatomies, artifacts and low contrasts among tiss…
Pyramid Network with Online Hard Example Mining for Accurate Left Atrium Segmentation
Cheng Bian, Xin Yang, Jianqiang Ma +5
Accurately segmenting left atrium in MR volume can benefit the ablation procedure of atrial fibrillation. Traditional automated solutions often fail in relieving experts from the l…
Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model
Xiaomeng Li, Lequan Yu, Hao Chen +2
Automatic skin lesion segmentation on dermoscopic images is an essential component in computer-aided diagnosis of melanoma. Recently, many fully supervised deep learning based meth…
EC-Net: an Edge-aware Point set Consolidation Network
Lequan Yu, Xianzhi Li, Chi-Wing Fu +2
Point clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required to be consolidated. In this paper, we present the first deep learning based edge-aware…
Deeply Supervised Rotation Equivariant Network for Lesion Segmentation in Dermoscopy Images
Xiaomeng Li, Lequan Yu, Chi-Wing Fu +1
Automatic lesion segmentation in dermoscopy images is an essential step for computer-aided diagnosis of melanoma. The dermoscopy images exhibits rotational and reflectional symmetr…
Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation
Cheng Chen, Qi Dou, Hao Chen +1
In spite of the compelling achievements that deep neural networks (DNNs) have made in medical image computing, these deep models often suffer from degraded performance when being a…
MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images
Simon Graham, Hao Chen, Jevgenij Gamper +5
The analysis of glandular morphology within colon histopathology images is an important step in determining the grade of colon cancer. Despite the importance of this task, manual s…
SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
Xiaowei Hu, Xuemiao Xu, Yongjie Xiao +4
Vision-based vehicle detection approaches achieve incredible success in recent years with the development of deep convolutional neural network (CNN). However, existing CNN based al…
Direction-aware Spatial Context Features for Shadow Detection and Removal
Xiaowei Hu, Chi-Wing Fu, Lei Zhu +2
Shadow detection and shadow removal are fundamental and challenging tasks, requiring an understanding of the global image semantics. This paper presents a novel deep neural network…
Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss
Qi Dou, Cheng Ouyang, Cheng Chen +2
Convolutional networks (ConvNets) have achieved great successes in various challenging vision tasks. However, the performance of ConvNets would degrade when encountering the domain…
PU-Net: Point Cloud Upsampling Network
Lequan Yu, Xianzhi Li, Chi-Wing Fu +2
Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data. In this paper, we present a data-driven point cloud ups…