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BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis
Hejie Cui · Wei Dai · Yanqiao Zhu · Xiaoxiao Li · Lifang He · Carl Yang

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in Interpretable Machine Learning in Healthcare »

Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience. GNNs are promising to model complicated network data, but they are prone to overfitting and suffer from poor interpretability, which prevents their usage in decision-critical scenarios like healthcare. To bridge this gap, we propose BrainNNExplainer, an interpretable GNN framework for brain network analysis. It is mainly composed of two jointly learned modules: a backbone prediction model that is specifically designed for brain networks and an explanation generator that highlights disease-specific prominent brain network connections. Extensive experimental results with visualizations on two challenging disease prediction datasets demonstrate the unique interpretability and outstanding performance of BrainNNExplainer.

Author Information

Hejie Cui (Emory University)

Hi there! This is Hejie Cui (pronounced as “He-jay Tsuee”, 崔鹤洁 in Chinese). I also go by the name Kelly. I am a second-year Ph.D. student in Computer Science at Emory University, under the supervision of Dr. Carl Yang in Emory Graph Mining Lab. I have also been working with Dr. Eugene Agichtein in Emory Intelligent Information Access Lab. Before joining Emory, I got my bachelor’s degree in Software Engineering from Tongji University, where I was working with Dr. Lin Zhang. My current research interests lie in machine learning with an emphasis on graph representation learning and its application to multi-modality data and brain network analysis.

Wei Dai (Emory University)
Yanqiao Zhu (Institution of Automation, Chinese Academy of Sciences)
Xiaoxiao Li (The University of British Columbia)
Lifang He (Lehigh University)
Carl Yang (Emory University)

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