49 citations · 129 across the 12 of their papers we have counts for
12 papers
LD-RSVIS: A Large-Scale and Diverse Benchmark for Referring Surgical Video Instrument Segmentation
Zan Wang, Yunhe Feng, Dong Nie +4
Referring surgical video instrument segmentation (RSVIS) aims at segmenting the instrument in a surgical video, given a textual description. Despite recent progress, current models…
The Devil is in the Upsampling: Architectural Decisions Made Simpler for Denoising with Deep Image Prior
Yilin Liu, Jiang Li, Yunkui Pang +2
Deep Image Prior (DIP) shows that some network architectures naturally bias towards smooth images and resist noises, a phenomenon known as spectral bias. Image denoising is an imme…
Multi-Label Clinical Time-Series Generation via Conditional GAN
Chang Lu, Chandan K. Reddy, Ping Wang +2
In recent years, deep learning has been successfully adopted in a wide range of applications related to electronic health records (EHRs) such as representation learning and clinica…
Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI
Junwei Yang, Xiao-Xin Li, Feihong Liu +4
Recent studies on T1-assisted MRI reconstruction for under-sampled images of other modalities have demonstrated the potential of further accelerating MRI acquisition of other modal…
Automated Generation of Accurate \& Fluent Medical X-ray Reports
Hoang T. N. Nguyen, Dong Nie, Taivanbat Badamdorj +4
Our paper focuses on automating the generation of medical reports from chest X-ray image inputs, a critical yet time-consuming task for radiologists. Unlike existing medical re-por…
HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation
Kelei He, Chunfeng Lian, Bing Zhang +6
Accurate segmentation of the prostate is a key step in external beam radiation therapy treatments. In this paper, we tackle the challenging task of prostate segmentation in CT imag…
Hybrid Graph Neural Networks for Crowd Counting
Ao Luo, Fan Yang, Xin Li +4
Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale…
Dual Adversarial Learning with Attention Mechanism for Fine-grained Medical Image Synthesis
Dong Nie, Lei Xiang, Qian Wang +1
Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and risk, the acquisition of certain image modalities c…
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation
Xuhua Ren, Lichi Zhang, Sahar Ahmad +5
Semantic segmentation is essentially important to biomedical image analysis. Many recent works mainly focus on integrating the Fully Convolutional Network (FCN) architecture with s…
Semantic-guided Encoder Feature Learning for Blurry Boundary Delineation
Dong Nie, Dinggang Shen
Encoder-decoder architectures are widely adopted for medical image segmentation tasks. With the lateral skip connection, the models can obtain and fuse both semantic and resolution…
Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image
Lei Xiang, Qian Wang, Xiyao Jin +3
Recently, more and more attention is drawn to the field of medical image synthesis across modalities. Among them, the synthesis of computed tomography (CT) image from T1-weighted m…
Medical Image Synthesis with Context-Aware Generative Adversarial Networks
Dong Nie, Roger Trullo, Caroline Petitjean +2
Computed tomography (CT) is critical for various clinical applications, e.g., radiotherapy treatment planning and also PET attenuation correction. However, CT exposes radiation dur…