22 citations · 37 across the 16 of their papers we have counts for
17 papers
From Sampled Evidence to Radiology Reports: Objectives, Constraints, and Realization
Kun Zhao, Haoteng Tang, Chenghua Lin +1
Sampling multiple reports creates an inference problem: how should sampled evidence be converted into a final decision, and does a better structured decision yield a better report?…
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…
Artemis: Anatomy-Resolved inTervention for Eliminating Multimodal NeuroImage confounderS
Siyuan Dai, Yang Du, Kun Zhao +6
Multimodal neuroimaging, integrating functional connectivity from fMRI and structural connectivity from DTI, enables non-invasive analysis of brain networks using graph neural netw…
Deep Models, Shallow Alignment: Uncovering the Granularity Mismatch in Neural Decoding
Yang Du, Siyuan Dai, Yonghao Song +3
Neural visual decoding is a central problem in brain-computer interface research, aiming to reconstruct human visual perception and to elucidate the structure of neural representat…
HERO: Hierarchical Evidential Reasoning Optimization for Radiology Report Generation via Reason-then-Summarize
Kun Zhao, Guodong Liu, Hui Ji +7
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…
Why Text Prevails: Vision May Undermine Multimodal Medical Decision Making
Siyuan Dai, Lunxiao Li, Kun Zhao +6
With the rapid progress of large language models (LLMs), advanced multimodal large language models (MLLMs) have demonstrated impressive zero-shot capabilities on vision-language ta…
DRE: An Effective Dual-Refined Method for Integrating Small and Large Language Models in Open-Domain Dialogue Evaluation
Kun Zhao, Bohao Yang, Chen Tang +4
Large Language Models (LLMs) excel at many tasks but struggle with ambiguous scenarios where multiple valid responses exist, often yielding unreliable results. Conversely, Small La…
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…
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…
Constrained Multiview Representation for Self-supervised Contrastive Learning
Siyuan Dai, Kai Ye, Kun Zhao +3
Representation learning constitutes a pivotal cornerstone in contemporary deep learning paradigms, offering a conduit to elucidate distinctive features within the latent space and…
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model
Haoteng Tang, Guixiang Ma, Lei Guo +3
Recently brain networks have been widely adopted to study brain dynamics, brain development and brain diseases. Graph representation learning techniques on brain functional network…
Functional2Structural: Cross-Modality Brain Networks Representation Learning
Haoteng Tang, Xiyao Fu, Lei Guo +7
MRI-based modeling of brain networks has been widely used to understand functional and structural interactions and connections among brain regions, and factors that affect them, su…
PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images
Haozhe Jia, Haoteng Tang, Guixiang Ma +4
Automated and accurate segmentation of the infected regions in computed tomography (CT) images is critical for the prediction of the pathological stage and treatment response of CO…
Boundary-aware Graph Reasoning for Semantic Segmentation
Haoteng Tang, Haozhe Jia, Weidong Cai +3
In this paper, we propose a Boundary-aware Graph Reasoning (BGR) module to learn long-range contextual features for semantic segmentation. Rather than directly construct the graph…
CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning
Haoteng Tang, Guixiang Ma, Lifang He +2
Recent years have witnessed the emergence and flourishing of hierarchical graph pooling neural networks (HGPNNs) which are effective graph representation learning approaches for gr…
Adversarial Attack on Hierarchical Graph Pooling Neural Networks
Haoteng Tang, Guixiang Ma, Yurong Chen +4
Recent years have witnessed the emergence and development of graph neural networks (GNNs), which have been shown as a powerful approach for graph representation learning in many ta…