107 citations · 134 across the 14 of their papers we have counts for
14 papers
EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generation
Hadrien Reynaud, Alberto Gomez, Paul Leeson +2
Advances in deep learning have significantly enhanced medical image analysis, yet the availability of large-scale medical datasets remains constrained by patient privacy concerns.…
JVID: Joint Video-Image Diffusion for Visual-Quality and Temporal-Consistency in Video Generation
Hadrien Reynaud, Matthew Baugh, Mischa Dombrowski +3
We introduce the Joint Video-Image Diffusion model (JVID), a novel approach to generating high-quality and temporally coherent videos. We achieve this by integrating two diffusion…
EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing
Hadrien Reynaud, Qingjie Meng, Mischa Dombrowski +5
To make medical datasets accessible without sharing sensitive patient information, we introduce a novel end-to-end approach for generative de-identification of dynamic medical imag…
Mesh-based 3D Motion Tracking in Cardiac MRI using Deep Learning
Qingjie Meng, Wenjia Bai, Tianrui Liu +2
3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and diagnosis of cardiovascular diseases. Most of the pre…
MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI
Qingjie Meng, Chen Qin, Wenjia Bai +4
Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding a…
Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net
Tianrui Liu, Qingjie Meng, Jun-Jie Huang +3
Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content…
Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging
Qingjie Meng, Jacqueline Matthew, Veronika A. Zimmer +4
Deep neural networks exhibit limited generalizability across images with different entangled domain features and categorical features. Learning generalizable features that can form…
Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment
Qingjie Meng, Daniel Rueckert, Bernhard Kainz
Learning deep neural networks that are generalizable across different domains remains a challenge due to the problem of domain shift. Unsupervised domain adaptation is a promising…
Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening
Jeremy Tan, Anselm Au, Qingjie Meng +7
Prenatal screening with ultrasound can lower neonatal mortality significantly for selected cardiac abnormalities. However, the need for human expertise, coupled with the high volum…
Ultrasound Video Summarization using Deep Reinforcement Learning
Tianrui Liu, Qingjie Meng, Athanasios Vlontzos +3
Video is an essential imaging modality for diagnostics, e.g. in ultrasound imaging, for endoscopy, or movement assessment. However, video hasn't received a lot of attention in the…
Learning Cross-domain Generalizable Features by Representation Disentanglement
Qingjie Meng, Daniel Rueckert, Bernhard Kainz
Deep learning models exhibit limited generalizability across different domains. Specifically, transferring knowledge from available entangled domain features(source/target domain)…
Semi-supervised Learning of Fetal Anatomy from Ultrasound
Jeremy Tan, Anselm Au, Qingjie Meng +1
Semi-supervised learning methods have achieved excellent performance on standard benchmark datasets using very few labelled images. Anatomy classification in fetal 2D ultrasound is…
Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound
Qingjie Meng, Nick Pawlowski, Daniel Rueckert +1
One of the biggest challenges for deep learning algorithms in medical image analysis is the indiscriminate mixing of image properties, e.g. artifacts and anatomy. These entangled i…
Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging
Qingjie Meng, Matthew Sinclair, Veronika Zimmer +11
Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a stand…