79 citations · 341 across the 40 of their papers we have counts for
16 papers · 1 filter
Adversarial Medical Image with Hierarchical Feature Hiding
Qingsong Yao, Zecheng He, Yuexiang Li +4
Deep learning based methods for medical images can be easily compromised by adversarial examples (AEs), posing a great security flaw in clinical decision-making. It has been discov…
Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views
Dong Wei, Yawen Huang, Donghuan Lu +2
Background: View planning for the acquisition of cardiac magnetic resonance (CMR) imaging remains a demanding task in clinical practice. Purpose: Existing approaches to its automat…
MEPNet: A Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Images
Hong Wang, Minghao Zhou, Dong Wei +2
Sparse-view computed tomography (CT) has been adopted as an important technique for speeding up data acquisition and decreasing radiation dose. However, due to the lack of sufficie…
Cross-Modal Vertical Federated Learning for MRI Reconstruction
Yunlu Yan, Hong Wang, Yawen Huang +5
Federated learning enables multiple hospitals to cooperatively learn a shared model without privacy disclosure. Existing methods often take a common assumption that the data from d…
Orientation-Shared Convolution Representation for CT Metal Artifact Learning
Hong Wang, Qi Xie, Yuexiang Li +3
During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical trea…
Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction
Hong Wang, Yuexiang Li, Deyu Meng +1
Inspired by the great success of deep neural networks, learning-based methods have gained promising performances for metal artifact reduction (MAR) in computed tomography (CT) imag…
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation
Yao Zhang, Nanjun He, Jiawei Yang +6
Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) is desirable to joint learning of multimodal images. However, in clinical practice, it is not always possibl…
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…
Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training
Yi Lin, Zhiyong Qu, Hao Chen +6
Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on t…
InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images
Hong Wang, Yuexiang Li, Haimiao Zhang +2
During the computed tomography (CT) imaging process, metallic implants within patients often cause harmful artifacts, which adversely degrade the visual quality of reconstructed CT…
InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction
Hong Wang, Yuexiang Li, Haimiao Zhang +4
For the task of metal artifact reduction (MAR), although deep learning (DL)-based methods have achieved promising performances, most of them suffer from two problems: 1) the CT ima…
Residual Moment Loss for Medical Image Segmentation
Quanziang Wang, Renzhen Wang, Yuexiang Li +3
Location information is proven to benefit the deep learning models on capturing the manifold structure of target objects, and accordingly boosts the accuracy of medical image segme…
MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint
Xinpeng Xie, Jiawei Chen, Yuexiang Li +3
Domain shift between medical images from multicentres is still an open question for the community, which degrades the generalization performance of deep learning models. Generative…
Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation
Yuexiang Li, Jiawei Chen, Xinpeng Xie +2
Witnessing the success of deep learning neural networks in natural image processing, an increasing number of studies have been proposed to develop deep-learning-based frameworks fo…
Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation
Xing Tao, Yuexiang Li, Wenhui Zhou +2
Deep learning highly relies on the quantity of annotated data. However, the annotations for 3D volumetric medical data require experienced physicians to spend hours or even days fo…
OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images
Yu Chen, Jiawei Chen, Dong Wei +2
Deep learning models, such as the fully convolutional network (FCN), have been widely used in 3D biomedical segmentation and achieved state-of-the-art performance. Multiple modalit…