12 citations · 13 across the 12 of their papers we have counts for
7 papers · 1 filter
Flip Distribution Alignment VAE for Multi-Phase MRI Synthesis
Xiaoyan Kui, Qianmu Xiao, Qqinsong Li +3
Separating shared and independent features is crucial for multi-phase contrast-enhanced (CE) MRI synthesis. However, existing methods use deep autoencoder generators with low param…
An Efficient Model-Driven Groupwise Approach for Atlas Construction
Ziwei Zou, Bei Zou, Xiaoyan Kui +6
Atlas construction is fundamental to medical image analysis, offering a standardized spatial reference for tasks such as population-level anatomical modeling. While data-driven reg…
TFKAN: Time-Frequency KAN for Long-Term Time Series Forecasting
Xiaoyan Kui, Canwei Liu, Qinsong Li +4
Kolmogorov-Arnold Networks (KANs) are highly effective in long-term time series forecasting due to their ability to efficiently represent nonlinear relationships and exhibit local…
Mamba Based Feature Extraction And Adaptive Multilevel Feature Fusion For 3D Tumor Segmentation From Multi-modal Medical Image
Zexin Ji, Beiji Zou, Xiaoyan Kui +3
Multi-modal 3D medical image segmentation aims to accurately identify tumor regions across different modalities, facing challenges from variations in image intensity and tumor morp…
Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution
Xiaoyan Kui, Zexin Ji, Beiji Zou +5
High-resolution medical images can provide more detailed information for better diagnosis. Conventional medical image super-resolution relies on a single task which first performs…
Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution
Zexin Ji, Beiji Zou, Xiaoyan Kui +2
Convolutional neural networks and Transformer have made significant progresses in multi-modality medical image super-resolution. However, these methods either have a fixed receptiv…
A Comprehensive Survey on Magnetic Resonance Image Reconstruction
Xiaoyan Kui, Zijie Fan, Zexin Ji +4
Magnetic resonance imaging (MRI) reconstruction is a fundamental task aimed at recovering high-quality images from undersampled or low-quality MRI data. This process enhances diagn…