3 citations · 5 across the 13 of their papers we have counts for
14 papers
Trust the Edge, Doubt the Arrow: Auditing Validation Signals for Text-Mined Clinical Causal Knowledge Graphs
Su Lan, Shicheng Zhou, Yiqi Wang +3
Clinical causal retrieval-augmented generation systems increasingly use knowledge graphs (KGs) mined from notes, yet extracted assertions remain candidates that require audit for p…
M2LG-DG: A Multi-modal Local-Global Domain Generalization Framework for Cross-site Major Depressive Disorder Classification
Muhammad Asif Hasan, Yanming Zhu, Xuefei Yin +1
Classification models based on resting-state functional magnetic resonance imaging (rs-fMRI) often show lower performance at imaging sites not included during model development, wh…
Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction
Su Lan, Xuefei Yin, Yanming Zhu +1
Causal and intervention-based question answering is fundamental to advancing large language models (LLMs) toward reasoning beyond surface-level correlations and understanding under…
Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach
Yujing Liu, Yixin Liu, Yu Zheng +3
Generalist graph anomaly detection (GAD) aims to detect anomalies on unseen graphs without graph-specific retraining. Nevertheless, existing approaches primarily focus on aligning…
Distance-Aware Joint Spatio-Temporal Graph Contrastive Learning for Major Depressive Disorder Diagnosis
Muhammad Asif Hasan, Yanming Zhu, Xuefei Yin +1
Major depressive disorder (MDD) is a common neuropsychiatric condition whose accurate diagnosis from resting-state functional magnetic resonance imaging (rs-fMRI) remains difficult…
fMRI-Diffusion: Generating fMRI Time Series Via a Temporal Transformer Diffusion Model for Major Depressive Disorder Diagnosis
Muhammad Asif Hasan, Yanming Zhu, Xuefei Yin +1
Diagnosing Major Depressive Disorder (MDD) from functional magnetic resonance imaging (fMRI) using functional connectivity (FC) analysis requires large amounts of labeled data that…
Neuroscience-inspired Staged Representation Learning with Disentangled Coarse- and Fine-Grained Semantics for EEG Visual Decoding
Xiang Gao, Hui Tian, Yanming Zhu +2
Decoding visual information from electroencephalography (EEG) signals remains a fundamental challenge in brain-computer interfaces and medical rehabilitation. Existing EEG visual d…
CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection
Junjun Pan, Yixin Liu, Yu Zheng +3
Text-attributed graph fraud detection (TAGFD) plays a critical role in preventing fraudulent activities on online social and e-commerce platforms. However, to evade detection, frau…
Prompt-Free Lightweight SAM Adaptation for Histopathology Nuclei Segmentation with Strong Cross-Dataset Generalization
Muhammad Hassan Maqsood, Yanming Zhu, Alfred Lam +3
Histopathology nuclei segmentation is crucial for quantitative tissue analysis and cancer diagnosis. Although existing segmentation methods have achieved strong performance, they a…
DCG-Net: Dual Cross-Attention with Concept-Value Graph Reasoning for Interpretable Medical Diagnosis
Getamesay Dagnaw, Xuefei Yin, Muhammad Hassan Maqsood +2
Deep learning models have achieved strong performance in medical image analysis, but their internal decision processes remain difficult to interpret. Concept Bottleneck Models (CBM…
NCSTR: Node-Centric Decoupled Spatio-Temporal Reasoning for Video-based Human Pose Estimation
Quang Dang Huynh, Xuefei Yin, Andrew Busch +4
Video-based human pose estimation remains challenged by motion blur, occlusion, and complex spatiotemporal dynamics. Existing methods often rely on heatmaps or implicit spatio-temp…
Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey
Getamesay Haile Dagnaw, Yanming Zhu, Muhammad Hassan Maqsood +4
Explainable artificial intelligence (XAI) has become increasingly important in biomedical image analysis to promote transparency, trust, and clinical adoption of DL models. While s…
Privacy-Preserving in Medical Image Analysis: A Review of Methods and Applications
Yanming Zhu, Xuefei Yin, Alan Wee-Chung Liew +1
With the rapid advancement of artificial intelligence and deep learning, medical image analysis has become a critical tool in modern healthcare, significantly improving diagnostic…
RT-NeRV: Rethinking Hybrid Neural Representations for Video via Residual Tokenization
Yunjie Xu, Xiang Feng, Chengkai Wang +3
Neural Representations for Videos(NeRV) have emerged as a promising paradigm for video compression by representing videos as compact neural networks with efficient decoding. Hybrid…