6 papers
Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum
Yingying Zhang, Kun Zhao, Guodong Liu +10
Alzheimer's disease (AD) progresses as a continuous biological process, whereas most existing neuroimaging-based artificial intelligence methods remain limited to discrete diagnosi…
HERO: Hierarchical Evidential Reasoning Optimization for Radiology Report Generation via Reason-then-Summarize
Kun Zhao, Siyuan Dai, Guodong Liu +6
Multimodal Large Language Models (MLLMs) have substantially advanced Radiology Report Generation (RRG), yet aligning them through reinforcement learning (RL) remains challenging du…
R-GenIMA: Integrating Neuroimaging and Genetics with Interpretable Multimodal AI for Alzheimer's Disease Progression
Kun Zhao, Siyuan Dai, Yingying Zhang +9
Early detection of Alzheimer's disease (AD) requires models capable of integrating macro-scale neuroanatomical alterations with micro-scale genetic susceptibility, yet existing mul…
Zeus: Zero-shot LLM Instruction for Union Segmentation in Multimodal Medical Imaging
Siyuan Dai, Kai Ye, Guodong Liu +2
Medical image segmentation has achieved remarkable success through the continuous advancement of UNet-based and Transformer-based foundation backbones. However, clinical diagnosis…
A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases
Zhepeng Wang, Runxue Bao, Yawen Wu +6
Graph neural networks (GNNs) are powerful machine learning models designed to handle irregularly structured data. However, their generic design often proves inadequate for analyzin…
Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation
Haoteng Tang, Guodong Liu, Siyuan Dai +9
The MRI-derived brain network serves as a pivotal instrument in elucidating both the structural and functional aspects of the brain, encompassing the ramifications of diseases and…