87 citations · 106 across the 14 of their papers we have counts for
14 papers
ProSMA-UNet: Decoder Conditioning for Proximal-Sparse Skip Feature Selection
Chun-Wun Cheng, Yanqi Cheng, Peiyuan Jing +4
Medical image segmentation commonly relies on U-shaped encoder-decoder architectures such as U-Net, where skip connections preserve fine spatial detail by injecting high-resolution…
Mamba Neural Operator: Who Wins? Transformers vs. State-Space Models for PDEs
Chun-Wun Cheng, Jiahao Huang, Yi Zhang +3
Partial differential equations (PDEs) are widely used to model complex physical systems, but solving them efficiently remains a significant challenge. Recently, Transformers have e…
CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates
Liutao Yang, Jiahao Huang, Guang Yang +1
Sparse views X-ray computed tomography has emerged as a contemporary technique to mitigate radiation dose. Because of the reduced number of projection views, traditional reconstruc…
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…
HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
Andrey Bryutkin, Jiahao Huang, Zhongying Deng +3
We present a novel graph transformer framework, HAMLET, designed to address the challenges in solving partial differential equations (PDEs) using neural networks. The framework use…
The Missing U for Efficient Diffusion Models
Sergio Calvo-Ordonez, Chun-Wun Cheng, Jiahao Huang +4
Diffusion Probabilistic Models stand as a critical tool in generative modelling, enabling the generation of complex data distributions. This family of generative models yields reco…
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…
Is attention all you need in medical image analysis? A review
Giorgos Papanastasiou, Nikolaos Dikaios, Jiahao Huang +2
Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance…
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…
Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study
Jiahao Huang, Pedro F. Ferreira, Lichao Wang +11
In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the micro-structure of myocardial tissue in the living hear…
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…