8 citations · 8 across the 9 of their papers we have counts for
9 papers
MMAP: Multimodal Missing-Aware Pretraining for Longitudinal Alzheimer's Prediction
Fiona Kekwick, Matthew Baugh, Bernhard Kainz +2
Clinical decision making heavily relies on predicting the disease progression trajectory by seeking to understand patient's health status which is characterised by multimodal medic…
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…
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…
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)…
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…
SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation
Berke Doga Basaran, Paul M. Matthews, Wenjia Bai
Assessing lesions and tracking their progression over time in brain magnetic resonance (MR) images is essential for diagnosing and monitoring multiple sclerosis (MS). Machine learn…
SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints
Berke Doga Basaran, Xinru Zhang, Paul M. Matthews +1
Assessment of lesions and their longitudinal progression from brain magnetic resonance (MR) images plays a crucial role in diagnosing and monitoring multiple sclerosis (MS). Machin…
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…