6 papers
BrainLinear: A Linear Model for Brain Network Analysis in Sparse Tangent Subspaces
Sijing Wu, Dongyuan Li, Miaoting Huang +4
Functional connectome analysis examines brain-region interactions to understand and identify disorders such as autism spectrum disorder and Alzheimer's disease. Existing methods ty…
Structure Matters: Brain Graph Augmentation via Learnable Edge Masking for Data-efficient Psychiatric Diagnosis
Mujie Liu, Chenze Wang, Liping Chen +5
The limited availability of labeled brain network data makes it challenging to achieve accurate and interpretable psychiatric diagnoses. While self-supervised learning (SSL) offers…
Data-Efficient Psychiatric Disorder Detection via Self-supervised Learning on Frequency-enhanced Brain Networks
Mujie Liu, Mengchu Zhu, Qichao Dong +4
Psychiatric disorders involve complex neural activity changes, with functional magnetic resonance imaging (fMRI) data serving as key diagnostic evidence. However, data scarcity and…
Entropy Causal Graphs for Multivariate Time Series Anomaly Detection
Falih Gozi Febrinanto, Kristen Moore, Chandra Thapa +4
Many multivariate time series anomaly detection frameworks have been proposed and widely applied. However, most of these frameworks do not consider intrinsic relationships between…
Refined Causal Graph Structure Learning via Curvature for Brain Disease Classification
Falih Gozi Febrinanto, Adonia Simango, Chengpei Xu +4
Graph neural networks (GNNs) have been developed to model the relationship between regions of interest (ROIs) in brains and have shown significant improvement in detecting brain di…
Rehearsal with Auxiliary-Informed Sampling for Audio Deepfake Detection
Falih Gozi Febrinanto, Kristen Moore, Chandra Thapa +3
The performance of existing audio deepfake detection frameworks degrades when confronted with new deepfake attacks. Rehearsal-based continual learning (CL), which updates models us…