44 citations · 130 across the 28 of their papers we have counts for
8 papers · 1 filter
Lesion Guided Explainable Few Weak-shot Medical Report Generation
Jinghan Sun, Dong Wei, Liansheng Wang +1
Medical images are widely used in clinical practice for diagnosis. Automatically generating interpretable medical reports can reduce radiologists' burden and facilitate timely care…
Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation
Xinyu Shi, Dong Wei, Yu Zhang +5
Research into Few-shot Semantic Segmentation (FSS) has attracted great attention, with the goal to segment target objects in a query image given only a few annotated support images…
Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration
Jiashun Chen, Donghuan Lu, Yu Zhang +5
Recently, deep-learning-based approaches have been widely studied for deformable image registration task. However, most efforts directly map the composite image representation to s…
Learning Shape Priors by Pairwise Comparison for Robust Semantic Segmentation
Cong Xie, Hualuo Liu, Shilei Cao +4
Semantic segmentation is important in medical image analysis. Inspired by the strong ability of traditional image analysis techniques in capturing shape priors and inter-subject si…
Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation
Cheng Bian, Chenglang Yuan, Kai Ma +3
Due to the lack of properly annotated medical data, exploring the generalization capability of the deep model is becoming a public concern. Zero-shot learning (ZSL) has emerged in…
Deep Convolutional Neural Networks for Molecular Subtyping of Gliomas Using Magnetic Resonance Imaging
Dong Wei, Yiming Li, Yinyan Wang +2
Knowledge of molecular subtypes of gliomas can provide valuable information for tailored therapies. This study aimed to investigate the use of deep convolutional neural networks (D…
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network
Shuxin Wang, Shilei Cao, Zhizhong Chai +4
Fully convolutional neural networks have made promising progress in joint liver and liver tumor segmentation. Instead of following the debates over 2D versus 3D networks (for examp…
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images
Hong Liu, Dong Wei, Donghuan Lu +4
Automated surface segmentation of retinal layer is important and challenging in analyzing optical coherence tomography (OCT). Recently, many deep learning based methods have been d…