12 citations · 21 across the 12 of their papers we have counts for
14 papers
DINO-Med: A Unified Patch-Based Adaptation Framework for Multi-Modal Medical Image Analysis Applied to Liver Fibrosis Staging
Boya Wang, Ruizhe Li, Chao Chen +1
Adapting natural-image foundation models like DINOv3 to multi-modal medical imaging is challenging due to the significant domain gap between natural color images and multi-channel…
T* and Susceptibility Mapping as Indicators of Placental Health
Amy Turnbull, George Hutchinson, Louise Dewick +8
Objective(s): T* and susceptibility () MRI mapping provide complimentary measures of the haemodynamic environment in the placenta. The aims of this work were to use these si…
Semi-supervised Liver Segmentation and Patch-based Fibrosis Staging with Registration-aided Multi-parametric MRI
Boya Wang, Ruizhe Li, Chao Chen +1
Liver fibrosis poses a substantial challenge in clinical practice, emphasizing the necessity for precise liver segmentation and accurate disease staging. Based on the CARE Liver 20…
Standardizing Longitudinal Radiology Report Evaluation via Large Language Model Annotation
Xinyi Wang, Grazziela Figueredo, Ruizhe Li +1
Longitudinal information in radiology reports refers to the sequential tracking of findings across multiple examinations over time, which is crucial for monitoring disease progress…
Breast Cancer Neoadjuvant Chemotherapy Treatment Response Prediction Using Aligned Longitudinal MRI and Clinical Data
Rahul Ravi, Ruizhe Li, Tarek Abdelfatah +2
Aim: This study investigates treatment response prediction to neoadjuvant chemotherapy (NACT) in breast cancer patients, using longitudinal contrast-enhanced magnetic resonance ima…
Placental contractions in uncomplicated pregnancies
Louise Dewick, Amy L Turnbull, Kate F Walker +11
In 2020 we first described placental contractions, and we have now undertaken a study to characterise them and seek features that might automatically separate them from uterine con…
Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge
Kang Wang, Chen Qin, Zhang Shi +46
Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis. However, the efficacy of these models is highly dependent on…
A Unified Framework for Semi-Supervised Image Segmentation and Registration
Ruizhe Li, Grazziela Figueredo, Dorothee Auer +3
Semi-supervised learning, which leverages both annotated and unannotated data, is an efficient approach for medical image segmentation, where obtaining annotations for the whole da…
MrRegNet: Multi-resolution Mask Guided Convolutional Neural Network for Medical Image Registration with Large Deformations
Ruizhe Li, Grazziela Figueredo, Dorothee Auer +2
Deformable image registration (alignment) is highly sought after in numerous clinical applications, such as computer aided diagnosis and disease progression analysis. Deep Convolut…
A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data
Xinyi Wang, Grazziela Figueredo, Ruizhe Li +3
Automatic radiology report generation can alleviate the workload for physicians and minimize regional disparities in medical resources, therefore becoming an important topic in the…
Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRI
Ruizhe Li, Xin Chen
Cardiac Magnetic Resonance Imaging (MRI) plays an important role in the analysis of cardiac function. However, the acquisition is often accompanied by motion artefacts because of t…
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRI
Ruizhe Li, Matteo Bastiani, Dorothee Auer +2
Brain age estimation based on magnetic resonance imaging (MRI) is an active research area in early diagnosis of some neurodegenerative diseases (e.g. Alzheimer, Parkinson, Huntingt…
FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net
Mina Jafari, Ruizhe Li, Yue Xing +4
In this paper, we present a generic deep convolutional neural network (DCNN) for multi-class image segmentation. It is based on a well-established supervised end-to-end DCNN model,…
A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation
Ruizhe Li, Dorothee Auer, Christian Wagner +1
Deep learning based image segmentation has achieved the state-of-the-art performance in many medical applications such as lesion quantification, organ detection, etc. However, most…