87 citations · 106 across the 14 of their papers we have counts for
7 papers · 1 filter
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…