Quality-Aware Fuzzy Min-Max Neural Networks for Dynamic Brain Network Analysis

Dynamic functional connections (dFCs) have been widely used for the diagnosis of brain diseases. However, current dynamic brain network analysis methods ignore the fuzzy information of the brain network and the uncertainty arising from the inconsistent data quality of different windows, providing unreliable integration for multiple windows. In this paper, we propose a dynamic brain network analysis method based on quality-aware fuzzy min-max neural networks (QFMMNet). The individual window of dFCs is treated as a view, and we define three convolution filters to extract features from the brain network under the multi-view learning framework, thereby obtaining multi-view evidence for dFCs. We design multi-view fuzzy min-max neural networks (MFMM) based on fuzzy sets to deal with the fuzzy information of the brain network, which takes evidence as input patterns to generate hyperboxes and serves as the classification layer of each view. A quality-aware ensemble module is introduced to deal with uncertainty, which employs D-S theory to directly model the uncertainty and evaluate the dynamic quality-aware weighting of each view. Experiments on two real schizophrenia datasets demonstrate the effectiveness and advantages of our proposed method. Our codes are available at https://github.com/scurrytao/QFMMNet .