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首页> 外文会议>IEEE International Symposium on Biomedical Imaging >An Attention-Based Hybrid Deep Learning Framework Integrating Temporal Coherence And Dynamics For Discriminating Schizophrenia
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An Attention-Based Hybrid Deep Learning Framework Integrating Temporal Coherence And Dynamics For Discriminating Schizophrenia

机译：基于关注的混合深度学习框架，整合判断精神分裂症的时间一致性和动力学

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The heterogeneity of schizophrenia makes it difficult to discover reliable imaging biomarkers, and most existing fMRI-based classification methods fail to combine temporal coherence between brain regions and temporal dynamics of brain activity. Therefore, we proposed a unified Hybrid Deep Learning Framework that effectively integrates temporal Coherence and Dynamics (HDLFCD) to classify psychiatric disorders by combining C-RNN, DNN and SVM. An attention module was also introduced into the C-RNN model to improve classification accuracy and interpretability without increasing the computation complexity. An accuracy of 85% was achieved in a large multi-site WRI dataset with 542 healthy controls and 558 schizophrenia patients, in which striatum, dorsolateral prefrontal cortex and cerebellum were identified as the most group-discriminative brain regions by the attention module. Note that the proposed framework is an end-to-end general module, which not only shows high superiority in combining multiple sources of information, but also can be easily applied to integrate other multimodal data.
机译：精神分裂症的异质性使得难以发现可靠的成像生物标志物，并且大多数现有的基于FMRI的分类方法无法结合大脑区域与脑活动的时间动态之间的时间相干性。因此，我们提出了一个统一的混合深度学习框架，通过组合C-RNN，DNN和SVM，有效地整合了时间一致性和动力学（HDLFD）来对精神疾病进行分类。注意模块也被引入C-RNN模型，以提高分类准确性和解释性，而不会增加计算复杂性。在具有542名健康对照和558名精神分裂症患者的大型多场WRI数据集中实现了85％的精度，其中纹状体，背侧前额叶皮质和小脑被注意力模块被鉴定为最多的群体鉴别脑区域。请注意，所提出的框架是端到端的一般模块，它不仅在组合多个信息源时显示出高优势，而且还可以很容易地应用于集成其他多模式数据。

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