7 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…
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…
Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction
Niklas Bubeck, Suprosanna Shit, Chen Chen +8
Cardiac Magnetic Resonance (CMR) imaging is a critical tool for diagnosing and managing cardiovascular disease, yet its utility is often limited by the sparse acquisition of 2D sho…
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…
Topology Optimization in Medical Image Segmentation with Fast Euler Characteristic
Liu Li, Qiang Ma, Cheng Ouyang +3
Deep learning-based medical image segmentation techniques have shown promising results when evaluated based on conventional metrics such as the Dice score or Intersection-over-Unio…
The Developing Human Connectome Project: A Fast Deep Learning-based Pipeline for Neonatal Cortical Surface Reconstruction
Qiang Ma, Kaili Liang, Liu Li +6
The Developing Human Connectome Project (dHCP) aims to explore developmental patterns of the human brain during the perinatal period. An automated processing pipeline has been deve…