7 papers
AT-Attn: Temporal-Aware Cross-Attention for Longitudinal Multimodal Alzheimer's Disease Diagnosis
Xinyue Du, Yibo Liu, Zhenglei Zhou +3
In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits. Integrating these multimodal observations may i…
ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection
Qiuhui Chen, Jiaxiang Song, Shuai Tan +1
Deep learning-based industrial anomaly detectors often behave as black boxes, making it hard to justify decisions with physically meaningful defect evidence. We propose ZSG-IAD, a…
AD-Reasoning: Multimodal Guideline-Guided Reasoning for Alzheimer's Disease Diagnosis
Qiuhui Chen, Yushan Deng, Xuancheng Yao +1
Alzheimer's disease (AD) diagnosis requires integrating neuroimaging with heterogeneous clinical evidence and reasoning under established criteria, yet most multimodal models remai…
EMAD: Evidence-Centric Grounded Multimodal Diagnosis for Alzheimer's Disease
Qiuhui Chen, Xuancheng Yao, Zhenglei Zhou +2
Deep learning models for medical image analysis often act as black boxes, seldom aligning with clinical guidelines or explicitly linking decisions to supporting evidence. This is e…
HoloDx: Knowledge- and Data-Driven Multimodal Diagnosis of Alzheimer's Disease
Qiuhui Chen, Jintao Wang, Gang Wang +1
Accurate diagnosis of Alzheimer's disease (AD) requires effectively integrating multimodal data and clinical expertise. However, existing methods often struggle to fully utilize mu…
Enhancing 3D Medical Image Understanding with Pretraining Aided by 2D Multimodal Large Language Models
Qiuhui Chen, Xuancheng Yao, Huping Ye +1
Understanding 3D medical image volumes is critical in the medical field, yet existing 3D medical convolution and transformer-based self-supervised learning (SSL) methods often lack…