8 citations · 16 across the 7 of their papers we have counts for
9 papers
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…
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)…