[1]戴稳豪,丁卫平,尹涛,等.基于可信多视图关联融合的脑网络分析算法[J].智能系统学报,2026,21(2):553-564.[doi:10.11992/tis.202507026]
DAI Wenhao,DING Weiping,YIN Tao,et al.Brain network analysis algorithm based on trusted multiview association fusion[J].CAAI Transactions on Intelligent Systems,2026,21(2):553-564.[doi:10.11992/tis.202507026]
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DAI Wenhao,DING Weiping,YIN Tao,et al.Brain network analysis algorithm based on trusted multiview association fusion[J].CAAI Transactions on Intelligent Systems,2026,21(2):553-564.[doi:10.11992/tis.202507026]
基于可信多视图关联融合的脑网络分析算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
21
期数:
2026年第2期
页码:
553-564
栏目:
学术论文—人工智能基础
出版日期:
2026-03-05
- Title:
- Brain network analysis algorithm based on trusted multiview association fusion
- Keywords:
- multiview fusion; evidence theory; mutual information; uncertainty; dynamic functional connectivity; brain network analysis; brain disease diagnosis; multiview learning
- 分类号:
- TP18
- 摘要:
- 动态功能连接与多视图学习相结合的方法通常假设多视图数据质量高度一致，忽略了现实世界中多视图数据的异质性及不可信性。针对上述问题，本文提出了基于可信多视图关联融合的脑网络分析算法。利用局部－全局卷积滤波器提取多视图脑网络证据；构建多视图关联融合网络，通过跨视图关联性衡量信息共享程度，采用选择性策略保留包含最多共有信息的视图，动态剔除低质量数据。为平滑证据强度，设计动态信任评估机制，将证据不确定性与类别概率联合建模来量化可信度并与证据加权中和。在两个真实的精神分裂症数据集上进行了多组实验，结果验证了算法的有效性和先进性，显著提高了脑疾病诊断任务的分类性能。
- Abstract:
- Dynamic functional connectivity combined with multiview learning usually assumes that the quality of multiview data is highly consistent, ignoring the heterogeneity and untrustworthiness of multiview data in the real world. To address the above problems, a brain network analysis algorithm based on trusted multiview association fusion is proposed. First, a local-global convolutional filter is utilized to extract multiview brain network evidence; second, a multiview association fusion network is constructed to measure the degree of information sharing through cross-view correlation, and then a selective strategy is used to retain the views containing the most shared information and dynamically reject low-quality data. To smooth the strength of evidence, a dynamic trust assessment mechanism is designed to quantify trustworthiness and neutralize it with evidence weighting by jointly modeling evidence uncertainty and category probability. Multiple sets of experiments are conducted on two real schizophrenia datasets, and the results validate the effectiveness and sophistication of the algorithms, which significantly improve the classification performance of the brain disease diagnosis task.
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备注/Memo
收稿日期:2025-7-22。
基金项目:国家重点研发计划项目(2024YFE0202700); 国家自然科学基金项目(62576178, U2433216); 江苏省自然科学基金项目(BK20231337); 江苏省现代农业机械装备与技术推广项目(NJ2024-06); 江苏省研究生科研与实践创新计划项目(SJCX25_2008，KYCX24_3646，KYCX23_3393).
作者简介:戴稳豪，硕士研究生，主要研究方向为不确定性深度学习和脑网络分析。E-mail：dwh668802@163.com。;丁卫平，教授，博士生导师，主要研究方向为多模态机器学习、多粒度计算、演化计算和医学大数据分析。发表学术论文300余篇。E-mail：dwp9988@163.com。;尹涛，博士研究生，主要研究方向为超图神经网络和粒计算。E-mail：haszyt@163.com。
通讯作者:丁卫平. E-mail：dwp9988@163.com
更新日期/Last Update:
1900-01-01