62 citations · 381 across the 50 of their papers we have counts for
52 papers
Is Autoencoder Truly Applicable for 3D CT Super-Resolution?
Weixun Luo, Xiaodan Xing, Guang Yang
Featured by a bottleneck structure, autoencoder (AE) and its variants have been largely applied in various medical image analysis tasks, such as segmentation, reconstruction and de…
Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation
Zhaohu Xing, Liang Wan, Huazhu Fu +2
In recent years, Denoising Diffusion Models have demonstrated remarkable success in generating semantically valuable pixel-wise representations for image generative modeling. In th…
Adversarial Transformer for Repairing Human Airway Segmentation
Zeyu Tang, Nan Yang, Simon Walsh +1
Discontinuity in the delineation of peripheral bronchioles hinders the potential clinical application of automated airway segmentation models. Moreover, the deployment of such mode…
Deep Kronecker Network
Long Feng, Guang Yang
We propose Deep Kronecker Network (DKN), a novel framework designed for analyzing medical imaging data, such as MRI, fMRI, CT, etc. Medical imaging data is different from general i…
Review of data types and model dimensionality for cardiac DTI SMS-related artefact removal
Michael Tanzer, Sea Hee Yook, Guang Yang +2
As diffusion tensor imaging (DTI) gains popularity in cardiac imaging due to its unique ability to non-invasively assess the cardiac microstructure, deep learning-based Artificial…
Quasi-supervised Learning for Super-resolution PET
Guangtong Yang, Chen Li, Yudong Yao +2
Low resolution of positron emission tomography (PET) limits its diagnostic performance. Deep learning has been successfully applied to achieve super-resolution PET. However, common…
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation
Hao Li, Yang Nan, Javier Del Ser +1
Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer from low reliability and robustness. Uncertainty estimation is an efficient solution t…
A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans
Shiyi Wang, Guang Yang
Medical image classification and segmentation based on deep learning (DL) are emergency research topics for diagnosing variant viruses of the current COVID-19 situation. In COVID-1…
Large-Kernel Attention for 3D Medical Image Segmentation
Hao Li, Yang Nan, Javier Del Ser +1
Automatic segmentation of multiple organs and tumors from 3D medical images such as magnetic resonance imaging (MRI) and computed tomography (CT) scans using deep learning methods…
Explainable AI (XAI) in Biomedical Signal and Image Processing: Promises and Challenges
Guang Yang, Arvind Rao, Christine Fernandez-Maloigne +2
Artificial intelligence has become pervasive across disciplines and fields, and biomedical image and signal processing is no exception. The growing and widespread interest on the t…
Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives
Hao Li, Zeyu Tang, Yang Nan +1
Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, re…
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation
Wenbo Zhang, Guang Yang, He Huang +4
Glioma is the most common and aggressive brain tumor. Magnetic resonance imaging (MRI) plays a vital role to evaluate tumors for the arrangement of tumor surgery and the treatment…
Unsupervised Image Registration Towards Enhancing Performance and Explainability in Cardiac And Brain Image Analysis
Chengjia Wang, Guang Yang, Giorgos Papanastasiou
Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as "modalities"). As each modality is designed to offer different anatomical and functional cli…
Fast MRI Reconstruction: How Powerful Transformers Are?
Jiahao Huang, Yinzhe Wu, Huanjun Wu +1
Magnetic resonance imaging (MRI) is a widely used non-radiative and non-invasive method for clinical interrogation of organ structures and metabolism, with an inherently long scann…
Swin Transformer for Fast MRI
Jiahao Huang, Yingying Fang, Yinzhe Wu +6
Magnetic resonance imaging (MRI) is an important non-invasive clinical tool that can produce high-resolution and reproducible images. However, a long scanning time is required for…
AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis
Yutong Chen, Carola-Bibiane Schönlieb, Pietro Liò +6
Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence, deep n…
Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information
Jiahao Huang, Weiping Ding, Jun Lv +7
In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inh…
Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentation
Michael Yeung, Leonardo Rundo, Yang Nan +3
The Dice similarity coefficient (DSC) is both a widely used metric and loss function for biomedical image segmentation due to its robustness to class imbalance. However, it is well…
Synthetic Velocity Mapping Cardiac MRI Coupled with Automated Left Ventricle Segmentation
Xiaodan Xing, Yinzhe Wu, David Firmin +2
Temporal patterns of cardiac motion provide important information for cardiac disease diagnosis. This pattern could be obtained by three-directional CINE multi-slice left ventricul…
Focal Attention Networks: optimising attention for biomedical image segmentation
Michael Yeung, Leonardo Rundo, Evis Sala +2
In recent years, there has been increasing interest to incorporate attention into deep learning architectures for biomedical image segmentation. The modular design of attention mec…
Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation
Michael Yeung, Guang Yang, Evis Sala +2
Manual segmentation is used as the gold-standard for evaluating neural networks on automated image segmentation tasks. Due to considerable heterogeneity in shapes, colours and text…
Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data
Jun Chen, Heye Zhang, Raad Mohiaddin +4
Semi-supervised learning provides great significance in left atrium (LA) segmentation model learning with insufficient labelled data. Generalising semi-supervised learning to cross…
3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework
Xi Guan, Guang Yang, Jianming Ye +4
Background: Glioma is the most common brain malignant tumor, with a high morbidity rate and a mortality rate of more than three percent, which seriously endangers human health. The…
High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss
Guangyuan Li, Jun Lv, Xiangrong Tong +2
Magnetic resonance imaging (MRI) is an important medical imaging modality, but its acquisition speed is quite slow due to the physiological limitations. Recently, super-resolution…