87 citations · 106 across the 14 of their papers we have counts for
7 papers · 1 filter
Learning Task-Specific Sampling Strategy for Sparse-View CT Reconstruction
Liutao Yang, Jiahao Huang, Yingying Fang +4
Sparse-View Computed Tomography (SVCT) offers low-dose and fast imaging but suffers from severe artifacts. Optimizing the sampling strategy is an essential approach to improving th…
MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation
Jiahao Huang, Liutao Yang, Fanwen Wang +5
The recent Mamba model has shown remarkable adaptability for visual representation learning, including in medical imaging tasks. This study introduces MambaMIR, a Mamba-based model…
Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation
Lichao Wang, Jiahao Huang, Xiaodan Xing +7
This study proposes a pipeline that incorporates a novel style transfer model and a simultaneous super-resolution and segmentation model. The proposed pipeline aims to enhance diff…
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?
Jiahao Huang, Angelica Aviles-Rivero, Carola-Bibiane Schönlieb +1
Deep learning has shown the capability to substantially accelerate MRI reconstruction while acquiring fewer measurements. Recently, diffusion models have gained burgeoning interest…
Swin Deformable Attention Hybrid U-Net for Medical Image Segmentation
Lichao Wang, Jiahao Huang, Xiaodan Xing +1
Medical image segmentation is a crucial task in the field of medical image analysis. Harmonizing the convolution and multi-head self-attention mechanism is a recent research focus…
ViGU: Vision GNN U-Net for Fast MRI
Jiahao Huang, Angelica Aviles-Rivero, Carola-Bibiane Schonlieb +1
Deep learning models have been widely applied for fast MRI. The majority of existing deep learning models, e.g., convolutional neural networks, work on data with Euclidean or regul…
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution
Mingfeng Jiang, Minghao Zhi, Liying Wei +6
High-resolution magnetic resonance images can provide fine-grained anatomical information, but acquiring such data requires a long scanning time. In this paper, a framework called…