Shagufta Abid — accepted AI-conference papers

2026

BrainCGT: A Brain Graph Transformer for Modeling Causal Connectivity in Neurological Disorder Diagnosis

Ahsan Shehzad, Dongyu Zhang, Shagufta Abid, Shuo Yu, Xin Zheng, Hongfei Lin +1

IJCAI 2026

Brain connectivity analysis is a fundamental tool for identifying biomarkers and understanding of neurological disorders. Most existing approaches employ graph transformers over undirected functional connectivity networks, which are typically estimated using correlation statistics. Although effectiv

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2025

SpeechHGT: A Multimodal Hypergraph Transformer for Speech-Based Early Alzheimer’s Disease Detection

Shagufta Abid, Dongyu Zhang, Ahsan Shehzad, Jing Ren, Shuo Yu, Hongfei Lin +1

IJCAI 2025

Early detection of Alzheimer's disease (AD) through spontaneous speech analysis represents a promising, non-invasive diagnostic approach. Existing methods predominantly rely on fusion-based multimodal deep learning, effectively integrating linguistic and acoustic features. However, these methods ina

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