Filters








2 Hits in 1.8 sec

BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis [article]

Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang
2021 arXiv   pre-print
To bridge this gap, we propose BrainNNExplainer, an interpretable GNN framework for brain network analysis.  ...  Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience.  ...  Conclusion In this work, we propose BrainNNExplainer, an interpretable GNN framework for brain network based disease analysis, which consists of a brain network oriented GNN predictor and a disease analysis  ... 
arXiv:2107.05097v1 fatcat:zi3wmur7sbeg5f62sfuqv3uwma

BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck [article]

Kaizhong Zheng, Shujian Yu, Baojuan Li, Robert Jenssen, Badong Chen
2022 arXiv   pre-print
In this work, we propose BrainIB, a new graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI), by leveraging the famed Information Bottleneck (IB) principle.  ...  Developing a new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus.  ...  Interpretation Analysis 1) Disease-Specific Brain Network Connections: To order to evaluate the interpretation of BrainIB, we further investigate the capability of IB-subgraph on interpreting the property  ... 
arXiv:2205.03612v1 fatcat:xqpyxz6z3bgatmmj4t32bkzc4i