8 citations · 10 across the 12 of their papers we have counts for
12 papers
CPAgents: Agentic Composite Phenotype Generation for Cardiac Disease Association
Zuoou Li, Wenlong Zhao, Kelly Yu +5
Identifying robust associations between cardiac imaging phenotypes and clinical diseases is fundamental to population-scale cardiovascular research and reliable risk stratification…
Wasserstein Equilibrium Decoding for Reliable Medical Visual Question Answering
Luca Hagen, Johanna P. Müller, Weitong Zhang +2
Small vision-language models (2-8B) are well-suited for clinical deployment due to privacy constraints, limited connectivity, and low-latency requirements favouring on-device or on…
Cardiac Mesh Flow: One-Step Generation of 3D+t Cardiac Four-Chamber Meshes via Flow Matching
Qiang Ma, Qingjie Meng, Mengyun Qiao +3
Spatio-temporal (3D+t) generative modelling of cardiac shape and motion is crucial for understanding heart structure and function at population scale. Existing generative models fo…
Learning a dynamic four-chamber shape model of the human heart for 95,695 UK Biobank participants
Qiang Ma, Qingjie Meng, Yicheng Wu +6
The human heart is a sophisticated system composed of four cardiac chambers with distinct shapes, which function in a coordinated manner. Existing shape models of the heart mainly…
A unified framework for geometry-independent operator learning in cardiac electrophysiology simulations
Bei Zhou, Cesare Corrado, Shuang Qian +9
Learning neural operators on heterogeneous and irregular geometries remains a fundamental challenge, as existing approaches typically rely on structured discretisations or explicit…
CardiacFlow: 3D+t Four-Chamber Cardiac Shape Completion and Generation via Flow Matching
Qiang Ma, Qingjie Meng, Mengyun Qiao +3
Learning 3D+t shape completion and generation from multi-view cardiac magnetic resonance (CMR) images requires a large amount of high-resolution 3D whole-heart segmentations (WHS)…
Multimodal Conditional MeshGAN for Personalized Aneurysm Growth Prediction
Long Chen, Ashiv Patel, Mengyun Qiao +8
Personalized, accurate prediction of aortic aneurysm progression is essential for timely intervention but remains challenging due to the need to model both subtle local deformation…
Towards Effective MLLM Jailbreaking Through Balanced On-Topicness and OOD-Intensity
Zuoou Li, Weitong Zhang, Jingyuan Wang +4
Multimodal large language models (MLLMs) are widely used in vision-language reasoning tasks. However, their vulnerability to adversarial prompts remains a serious concern, as safet…
Multi-Agent Reasoning for Cardiovascular Imaging Phenotype Analysis
Weitong Zhang, Mengyun Qiao, Chengqi Zang +4
Identifying associations between imaging phenotypes, disease risk factors, and clinical outcomes is essential for understanding disease mechanisms. However, traditional approaches…
A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics
Mengyun Qiao, Kathryn A McGurk, Shuo Wang +3
Understanding the structure and motion of the heart is crucial for diagnosing and managing cardiovascular diseases, the leading cause of global death. There is wide variation in ca…
A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts
Xinru Zhang, Ni Ou, Berke Doga Basaran +8
Brain lesion segmentation plays an essential role in neurological research and diagnosis. As brain lesions can be caused by various pathological alterations, different types of bra…
T1/T2 relaxation temporal modelling from accelerated acquisitions using a Latent Transformer
Fanwen Wang, Michael Tanzer, Mengyun Qiao +4
Quantitative cardiac magnetic resonance T1 and T2 mapping enable myocardial tissue characterisation but the lengthy scan times restrict their widespread clinical application. We pr…