Brain network classification using resting-state functional magnetic resonance imaging (rs-fMRI) is an effective analytical method for diagnosing brain diseases. In recent years, brain network classification methods based on deep learning have attracted increasing attention. However, these methods only consider the spatial topological characteristics of the brain network but ignore its proximity relationships in semantic space. To overcome this problem, we propose a novel brain network classification method based on deep graph hashing learning named BNC-DGHL. Specifically, we first extract the deep features of the brain network and then learn a graph hash function based on clinical phenotype labels and the similarity of diagnostic labels. Secondly, we use the learned graph hash function to convert deep features into hash codes, which can maintain the original semantic spatial relationships. Finally, we calculate the distance between hash codes to obtain the predicted category of the brain network. Experimental results on ABIDE I, ABIDE II, and ADHD-200 datasets demonstrate that our method achieves better classification performance of brain diseases compared with some state-of-the-art methods, and the abnormal functional connectivities between brain regions identified may serve as biomarkers associated with related brain diseases.

中文翻译：

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基于深度图哈希学习的功能脑网络分类


使用静息态功能磁共振成像（rs-fMRI）进行脑网络分类是诊断脑部疾病的有效分析方法。近年来，基于深度学习的脑网络分类方法越来越受到关注。然而，这些方法只考虑了脑网络的空间拓扑特征，而忽略了其在语义空间中的邻近关系。为了克服这个问题，我们提出了一种基于深度图哈希学习的新型脑网络分类方法，名为 BNC-DGHL。具体来说，我们首先提取大脑网络的深层特征，然后根据临床表型标签和诊断标签的相似性学习图哈希函数。其次，我们使用学习到的图哈希函数将深层特征转换为哈希码，这可以保持原始的语义空间关系。最后，我们计算哈希码之间的距离，以获得大脑网络的预测类别。 ABIDE I、ABIDE II 和 ADHD-200 数据集上的实验结果表明，与一些最先进的方法相比，我们的方法实现了更好的脑部疾病分类性能，并且识别出的大脑区域之间的异常功能连接可以作为生物标志物与相关脑部疾病有关。