activity
20242026
most citedCMViM: Contrastive Masked Vim Autoencoder for 3D Multi-modal Representation Learning for AD classification

3 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

PRA-PoE: Robust Multimodal Alzheimer's Diagnosis with Arbitrary Missing Modalities

Guangqian Yang, Ye Du, Wenlong Hou +2

Missing modalities are prevalent in real-world Alzheimer's disease (AD) assessment and pose a significant challenge to multimodal learning, particularly when the distribution of ob…

cs.CV2026

BrainAnytime: Anatomy-Aware Cross-Modal Pretraining for Brain Image Analysis with Arbitrary Modality Availability

Guangqian Yang, Tong Ding, Wenlong Hou +4

Clinical diagnostic workups typically follow a modality escalation pathway: after initial clinical evaluation, clinicians begin with routine structural imaging (e.g., MRI), selecti…

cs.MA2026

AD-CARE: A Guideline-grounded, Modality-agnostic LLM Agent for Real-world Alzheimer's Disease Diagnosis with Multi-cohort Assessment, Fairness Analysis, and Reader Study

Wenlong Hou, Sheng Bi, Guangqian Yang +16

Alzheimer's disease (AD) is a growing global health challenge as populations age, and timely, accurate diagnosis is essential to reduce individual and societal burden. However, rea…

eess.IV2025

ADAgent: LLM Agent for Alzheimer's Disease Analysis with Collaborative Coordinator

Wenlong Hou, Guangqian Yang, Ye Du +5

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Early and precise diagnosis of AD is crucial for timely intervention and treatment planning to…

cs.CV2024★ 3 cited

CMViM: Contrastive Masked Vim Autoencoder for 3D Multi-modal Representation Learning for AD classification

Guangqian Yang, Kangrui Du, Zhihan Yang +3

Alzheimer's disease (AD) is an incurable neurodegenerative condition leading to cognitive and functional deterioration. Given the lack of a cure, prompt and precise AD diagnosis is…