1 citations · 1 across the 6 of their papers we have counts for
6 papers
HUR-MACL: High-Uncertainty Region-Guided Multi-Architecture Collaborative Learning for Head and Neck Multi-Organ Segmentation
Xiaoyu Liu, Siwen Wei, Linhao Qu +4
Accurate segmentation of organs at risk in the head and neck is essential for radiation therapy, yet deep learning models often fail on small, complexly shaped organs. While hybrid…
DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI Reconstruction
Yucong Meng, Zhiwei Yang, Zhijian Song +1
The accelerated MRI reconstruction poses a challenging ill-posed inverse problem due to the significant undersampling in k-space. Deep neural networks, such as CNNs and ViTs, have…
Boosting ViT-based MRI Reconstruction from the Perspectives of Frequency Modulation, Spatial Purification, and Scale Diversification
Yucong Meng, Zhiwei Yang, Yonghong Shi +1
The accelerated MRI reconstruction process presents a challenging ill-posed inverse problem due to the extensive under-sampling in k-space. Recently, Vision Transformers (ViTs) hav…
Continuous K-space Recovery Network with Image Guidance for Fast MRI Reconstruction
Yucong Meng, Zhiwei Yang, Minghong Duan +2
Magnetic resonance imaging (MRI) is a crucial tool for clinical diagnosis while facing the challenge of long scanning time. To reduce the acquisition time, fast MRI reconstruction…
FANCL: Feature-Guided Attention Network with Curriculum Learning for Brain Metastases Segmentation
Zijiang Liu, Xiaoyu Liu, Linhao Qu +1
Accurate segmentation of brain metastases (BMs) in MR image is crucial for the diagnosis and follow-up of patients. Methods based on deep convolutional neural networks (CNNs) have…
Deep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentation
Xiaoyu Liu, Linhao Qu, Ziyue Xie +2
The task of labeling multiple organs for segmentation is a complex and time-consuming process, resulting in a scarcity of comprehensively labeled multi-organ datasets while the eme…