355 citations · 1.5k across the 25 of their papers we have counts for
25 papers
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space
Quande Liu, Cheng Chen, Jing Qin +2
Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection. While at clinical deployment, the models trai…
Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment
Xueying Shi, Yueming Jin, Qi Dou +2
Automated surgical gesture recognition is of great importance in robot-assisted minimally invasive surgery. However, existing methods assume that training and testing data are from…
Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation
Youyi Song, Lei Zhu, Baiying Lei +4
Segmenting overlapping cytoplasm of cells in cervical smear images is a clinically essential task, for quantitatively measuring cell-level features in order to diagnose cervical ca…
CNN in CT Image Segmentation: Beyound Loss Function for Expoliting Ground Truth Images
Youyi Song, Zhen Yu, Teng Zhou +4
Exploiting more information from ground truth (GT) images now is a new research direction for further improving CNN's performance in CT image segmentation. Previous methods focus o…
Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion
Cheng Chen, Qi Dou, Yueming Jin +3
Accurate medical image segmentation commonly requires effective learning of the complementary information from multimodal data. However, in clinical practice, we often encounter th…
Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation
Cheng Chen, Qi Dou, Hao Chen +2
Unsupervised domain adaptation has increasingly gained interest in medical image computing, aiming to tackle the performance degradation of deep neural networks when being deployed…
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis
Xueying Shi, Qi Dou, Cheng Xue +3
Automated skin lesion analysis is very crucial in clinical practice, as skin cancer is among the most common human malignancy. Existing approaches with deep learning have achieved…
Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper Contaminations
Luming Liang, Sen Deng, Lionel Gueguen +3
We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by the salt-and-pepper (s&p) noise. A median layer…
Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis
Yueming Jin, Huaxia Li, Qi Dou +4
Surgical tool presence detection and surgical phase recognition are two fundamental yet challenging tasks in surgical video analysis and also very essential components in various a…
Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound
Yi Wang, Haoran Dou, Xiaowei Hu +7
Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developi…
Multi-Kernel Correntropy for Robust Learning
Badong Chen, Yuqing Xie, Xin Wang +3
As a novel similarity measure that is defined as the expectation of a kernel function between two random variables, correntropy has been successfully applied in robust machine lear…
Robust Matrix Completion via Maximum Correntropy Criterion and Half Quadratic Optimization
Yicong He, Fei Wang, Yingsong Li +2
Robust matrix completion aims to recover a low-rank matrix from a subset of noisy entries perturbed by complex noises, where traditional methods for matrix completion may perform p…
Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation
Cheng Chen, Qi Dou, Hao Chen +2
This paper presents a novel unsupervised domain adaptation framework, called Synergistic Image and Feature Adaptation (SIFA), to effectively tackle the problem of domain shift. Dom…
Structure-Aware 3D Hourglass Network for Hand Pose Estimation from Single Depth Image
Fuyang Huang, Ailing Zeng, Minhao Liu +2
In this paper, we propose a novel structure-aware 3D hourglass network for hand pose estimation from a single depth image, which achieves state-of-the-art results on MSRA and NYU d…
Granger Causality Analysis Based on Quantized Minimum Error Entropy Criterion
Badong Chen, Rongjin Ma, Siyu Yu +2
Linear regression model (LRM) based on mean square error (MSE) criterion is widely used in Granger causality analysis (GCA), which is the most commonly used method to detect the ca…
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…
SFCN-OPI: Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction
Yanning Zhou, Qi Dou, Hao Chen +2
Cell nuclei detection and fine-grained classification have been fundamental yet challenging problems in histopathology image analysis. Due to the nuclei tiny size, significant inte…
Direction-aware Spatial Context Features for Shadow Detection
Xiaowei Hu, Lei Zhu, Chi-Wing Fu +2
Shadow detection is a fundamental and challenging task, since it requires an understanding of global image semantics and there are various backgrounds around shadows. This paper pr…
Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning
Qi Dou, Hao Chen, Yueming Jin +3
In this paper, we propose a novel framework with 3D convolutional networks (ConvNets) for automated detection of pulmonary nodules from low-dose CT scans, which is a challenging ye…
ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images
Huangjing Lin, Hao Chen, Qi Dou +3
Lymph node metastasis is one of the most significant diagnostic indicators in breast cancer, which is traditionally observed under the microscope by pathologists. In recent years,…
Robust Learning with Kernel Mean p-Power Error Loss
Badong Chen, Lei Xing, Xin Wang +2
Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing. In this paper, we define a nonsecon…
Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images
Xin Yang, Lequan Yu, Lingyun Wu +4
Boundary incompleteness raises great challenges to automatic prostate segmentation in ultrasound images. Shape prior can provide strong guidance in estimating the missing boundary,…
3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
Qi Dou, Hao Chen, Yueming Jin +3
Automatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment. In this paper, we present a…