8 papers · 1 filter
PAS-Mamba: Phase-Amplitude-Spatial State Space Model for MRI Reconstruction
Xiaoyan Kui, Zijie Fan, Zexin Ji +5
Joint feature modeling in both the spatial and frequency domains has become a mainstream approach in MRI reconstruction. However, existing methods generally treat the frequency dom…
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…
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…
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…
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution
Zexin Ji, Beiji Zou, Xiaoyan Kui +2
In this paper, we propose a self-prior guided Mamba-UNet network (SMamba-UNet) for medical image super-resolution. Existing methods are primarily based on convolutional neural netw…
Deform-Mamba Network for MRI Super-Resolution
Zexin Ji, Beiji Zou, Xiaoyan Kui +2
In this paper, we propose a new architecture, called Deform-Mamba, for MR image super-resolution. Unlike conventional CNN or Transformer-based super-resolution approaches which enc…
ChebMixer: Efficient Graph Representation Learning with MLP Mixer
Xiaoyan Kui, Haonan Yan, Qinsong Li +2
Graph neural networks have achieved remarkable success in learning graph representations, especially graph Transformer, which has recently shown superior performance on various gra…