融合遗传算法和图神经网络的抑郁症智能诊断  

Intelligent diagnosis of depression by integrating genetic algorithm and graph neural network

作　　者：龙丹[1] 章梦达 应仁辉 陈丰农 邵岚[1] 谢璩 罗聪[1] Long Dan;Zhang Mengda;Ying Renhui;Chen Fengnong;Shao Lan;Xie Qu;Luo Cong(Zhejiang Cancer Hospital,Hangzhou Institute of Medicine(HIM),Chinese Academy of Sciences,Hangzhou 310022,China;School of Automation,Hangzhou Dianzi University,Hangzhou 310018,China)

机构地区：[1]浙江省肿瘤医院中国科学院杭州医学研究所,杭州310022 [2]杭州电子科技大学自动化学院,杭州310018

出　　处：《中国图象图形学报》2024年第11期3476-3486,共11页Journal of Image and Graphics

基　　金：浙江省医药卫生科技项目(2023KY596);浙江省基础公益研究计划项目(LGF22H160084);浙江省中医药科学研究基金项目(B类20212B037)。

摘　　要：目的构建基于脑网络的抑郁症智能诊断模型是一项具有挑战性的任务。近年来,图神经网络(graph neu⁃ral network,GNN)越来越多地应用于图的分类任务中,大部分GNN研究都只是对单一空间(样本空间或者特征空间)进行建模,导致模型分类性能不够好,本文提出一种基于遗传算法(genetic algorithm,GA)和GNN的多空间融合算法来对抑郁症患者进行智能诊断。方法模型采用留一站点交叉验证来确保模型的泛化性。脑网络的构建是基于Pearson相关的功能连接方法。整个算法以遗传算法作为主要框架,其中适应度函数是基于图卷积网络(graph convolutional network,GCN)分类算法,通过搜索个体间相似性阈值来找到具有最高分辨力的GCN。GCN由两个网络串联组成,一个网络获取受试者特征空间信息,另一个提取受试者之间样本空间的信息,最后通过两级GCN的联合学习实现分类。结果所有数据来源于The REST-meta-MDD项目,一共有来自10个站点1160个受试者功能磁共振数据纳入本实验(男434、女726)。实验结果显示,本文提出的分类器准确性、精度和受试者特征曲线(receiver operating characteristic,ROC)下面积分别为64.72%、69.69%和64.58%,优于其他主流算法。结论与其他算法相比,本文提出的算法融合了传统模型和深度学习模型的优点,获得了最佳的分类性能,未来很有可能为临床抑郁症诊断提供重要依据。Objective Depression is currently one of the most common neuropsychiatric disorders in the world.However,its pathophysiological mechanisms are still unclear.The diagnosis of depression in clinical practice typically depends on neuropsychological scores and treatment responses,lacking objective evaluation tools,resulting in low consistency in diagnosis.In recent years,an increasing number of people have begun to use machine learning technology to extract imaging biomarkers for the intelligent diagnosis of depression due to the capability of functional magnetic resonance imaging to provide in vivo brain function and structural information.The brain network-based model has remarkable potential as an imaging marker for effectively distinguishing depression from normal controls.Graph neural networks(GNNs)are highly suitable for graph classification tasks because they directly acquire graph structure information and maintain the topological characteristics of the graph during task execution.However,most GNN studies only model a single space(sample or feature space),and the aggregation of GNN information can lead to over-smooth effects,resulting in poor model classification performance.This study aims to integrate multiple feature space information and propose a multispace fusion algorithm for the intelligent diagnosis of depression patients.Method Leave-one-site cross-validation(LOSCV)is used to ensure the generalization of the model.The data are first preprocessed,and then a brain network is constructed using Pearson-related functional connectivity methods.The entire algorithm is mainly based on a genetic algorithm(GA),where the fitness function is a classification algorithm based on a graph convolutional network(GCN).The solution space searched by GA is the similarity between the subject networks.The main steps of GA are as follows:1)Set the search range of the solution space[0.05,0.7];2)Generate an initial population;3)Based on LOSCV,GCN is used to classify the data,with the F1 value as the target value of the fitne

关 键 词：抑郁症 图卷积网络(GCN) 智能诊断 融合算法 个体相似性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]