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ADHD diagnosis guided by functional brain networks combined with domain knowledge

Computers in Biology and Medicine 2024年 177卷 108611-108611页

作者： Cao, Chunhong Fu, Huawei Li, Gai Wang, Mengyang Gao, Xieping MOE Key Laboratory of Intelligent Computing and Information Processing Xiangtan University Xiangtan411100 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

Utilizing functional magnetic resonance imaging (fMRI) to model functional brain networks (FBNs) is increasingly prominent in attention-deficit/hyperactivity disorder (ADHD) research, revealing neural impact and mechanisms through the exploration of activated brain regions. However, current FBNs-based methods face two major challenges. The primary challenge stems from the limitations of existing modeling methods in accurately capturing both regional correlations and long-distance dependencies (LDDs) within the dynamic brain, thereby affecting the diagnostic accuracy of FBNs as biomarkers. Additionally, limited sample size and class imbalance also pose a challenge to the learning performance of the model. To address the issues, we propose an automated diagnostic framework, which integrates modeling, multimodal fusion, and classification into a unified process. It aims to extract representative FBNs and efficiently incorporate domain knowledge to guide ADHD classification. Our work mainly includes three-fold: 1) A multi-head attention-based region-enhancement module (MAREM) is designed to simultaneously capture regional correlations and LDDs across the entire sequence of brain activity, which facilitates the construction of representative FBNs. 2) The multimodal supplementary learning module (MSLM) is proposed to integrate domain knowledge from phenotype data with FBNs from neuroimaging data, achieving information complementarity and alleviating the problems of insufficient medical data and unbalanced sample categories. 3) An ADHD automatic diagnosis framework guided by FBNs and domain knowledge (ADF-FAD) is proposed to help doctors make more accurate decisions, which is applied to the ADHD-200 dataset to confirm its effectiveness. The results indicate that the FBNs extracted by MAREM perform well in modeling and classification. After with MSLM, the model achieves accuracy of 92.4%, 74.4%, and 80% at NYU, PU, and KKI, respectively, demonstrating its ability to effecti

关键词： Domain Knowledge

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Improving Continual Few-shot Relation Extraction through Relational Knowledge Distillation and Prototype Augmentation 30

Improving Continual Few-shot Relation Extraction through Rel...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Zhiheng Zeng, Daojian Bai, Xue Institute of AI and Targeted International Communication Hunan Normal University Changsha China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Changsha Dworld AI Tech Co. Ltd. China

ISBN: (纸本)9782493814104

In this paper, we focus on the challenging yet practical problem of Continual Few-shot Relation Extraction (CFRE), which involves extracting relations in the continuous and iterative arrival of new data with only a few labeled examples. The main challenges in CFRE are overfitting due to few-shot learning and catastrophic forgetting caused by continual learning. To address these problems, we propose a novel framework called RK2DA, which seamlessly integrates prototype-based data augmentation and relational knowledge distillation. Specifically, RK2DA generates pseudo data by introducing Gaussian noise to the prototype embeddings and utilizes a novel two-phase multi-teacher relational knowledge distillation method to transfer diverse knowledge from different embedding spaces. Experimental results on the FewRel and TACRED datasets demonstrate that our method outperforms the state-of-the-art baselines. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Distillation

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Fuzzy Information Quantity Measurement and Feature Selection by Macrogranular Entropy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Zhu, Zhilin Zhang, Chucai Dai, Jianhua Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Changsha410081 China

Feature selection is an important data preprocessing process in artificial intelligence, which aims to eliminate redundant features while retaining essential features. Measuring feature significance and relevance between features is a significant challenge. Fuzzy information entropy is an extension of Shannon entropy. It is widely used for quantifying the information of fuzzy divisions. However, it has significant limitations, notably the lack of monotonicity in fuzzy conditional entropy measure of decision uncertainty in the feature selection process. We introduce a novel measurement Macrogranular Entropy (ME) and construct some generalized forms, such as conditional macrogranular entropy, mutual macrogranular information, and joint macrogranular entropy. The conditional macrogranular entropy exhibits monotonicity when measuring decision uncertainty. Additionally, we propose two feature selection algorithms: one based on Monotonic Conditional Macrogranular Entropy (MCME), and the other based on the Degree of Symmetric Association (ADSA). The ADSA algorithm and the MCME algorithm are compared against eight other feature selection algorithms through a series of experiments. The comparison was conducted based on classification performance using SVM and NB classifiers, as well as evaluation metrics including F1-score and Recall. In terms of all four evaluation metrics, ADSA and MCME achieved the top two rankings, respectively. Specifically, on the NB and SVM classifiers, the ADSA algorithm improves the average accuracy by 12.22% and 2.88% compared to the original feature set, while MCME improves the accuracy by 10.07% and 1.01%, respectively. Experimental comparisons demonstrate that ADSA algorithm effectively removes redundant information from the dataset during feature selection. © 2025 IEEE.

关键词： Feature Selection

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Online Multi-label Streaming Feature Selection with Missing Features by Dual-Space Consistency Information Measurement

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Dai, Jianhua Wang, Jie Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Changsha410081 China

Multi-label feature selection is critical to address the challenges of high dimensionality and computational complexity in multi-label learning. However, in some practical applications, a more complex challenge is the dynamic generation of features, known as streaming features. Although some scholars have studied multi-label streaming feature selection methods, the problem of missing features during dynamic feature generation in multi-label data is rarely considered. Hence, we propose an online multi-label streaming feature selection method for multilabel data containing missing features by dual-space consistency information measurement. Firstly, we construct the dual-space consistency information measurement, which can effectively capture the relationships between features and labels. Specifically, we consider it from the perspective of instance correlation, and propose a fuzzy α-tolerance relation in the feature space to directly deal with missing features, and introduce an improved neighbour similarity relation in the label space to capture the instance similarity between the label combinations. Secondly, based on the dual-space consistency information, we further introduce the consistency gain index to help identify those features with important information during the streaming feature selection process. Thirdly, by combining online significance analysis, online relevance analysis, and online redundancy analysis, a new streaming feature selection framework is constructed, which can comprehensively evaluate the newly arrived features in terms of strong consistency, weak consistency and irrelevance, so as to identify the most representative features in the continuously arriving feature streams. Finally, a series of comparative experiments on multiple benchmark datasets demonstrate the effectiveness and superiority of our proposed method. © 2025 IEEE.

关键词： Data consistency

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Online Multi-Label Streaming Feature Selection by Label Enhancement and Fuzzy Synergistic Discrimination Information

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IEEE Transactions on Fuzzy Systems 2025年

作者： Zou, Ligeng Zhou, Tong Dai, Jianhua Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Changsha410081 China

Online streaming feature selection is an effective approach for handling large-scale streaming data in real-world applications. However, many existing online streaming feature selection studies do not effectively leverage the correlation and hidden information in the label space. The measures used in streaming feature selection often face challenges such as computational complexity and insufficient information exploitation. To address these issues, we propose an online streaming feature selection method based on label enhancement and fuzzy synergistic discrimination information. Firstly, we define a novel label enhancement method based on label correlation, which does not require any prior knowledge of feature space. Secondly, we propose a new measure to evaluate the discriminative ability of multiple feature subsets for labels in fuzzy environment, called fuzzy synergistic discrimination information. It is bounded and has many interesting properties. The fuzzy synergistic discrimination information is computed directly based on fuzzy similarity relations, so the calculation is relatively simple. Thirdly, based on label enhancement and proposed fuzzy synergistic discrimination information, we construct a three-stage online multi-label streaming feature selection method, including online significance analysis, online relevance analysis and online redundancy analysis. Finally, comparative experiments on 12 datasets demonstrate the effectiveness and superiority of the proposed method. The influences of different feature sequences and these components on the algorithm's performance are further analyzed. © 1993-2012 IEEE.

关键词： Information hiding

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An Efficient WRF Framework for Discovering Risk Genes and Abnormal Brain Regions in Parkinson's Disease Based on Imaging Genetics Data

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Journal of Computer Science & Technology 2021年第2期36卷 361-374页

作者： Xia-An Bi Zhao-Xu Xing Rui-Hui Xu Xi Hu College of Information Science and Engineering Hunan Normal UniversityChangsha 410006China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha 410006China

As an emerging research field of brain science,multimodal data fusion analysis has attracted broader attention in the study of complex brain diseases such as Parkinson's disease(PD).However,current studies primarily lie with detecting the association among different modal data and reducing data *** data mining method after fusion and the overall analysis framework are *** this study,we propose a weighted random forest(WRF)model as the feature screening *** interactions between genes and brain regions are detected as input multimodal fusion features by the correlation analysis *** implement sample classification and optimal feature selection based on WRF,and construct a multimodal analysis framework for exploring the pathogenic factors of *** experimental results in Parkinson's Progression Markers Initiative(PPMI)database show that WRF performs better compared with some advanced methods,and the brain regions and genes related to PD are *** fusion of multi-modal data can improve the classification of PD patients and detect the pathogenic factors more comprehensively,which provides a novel perspective for the diagnosis and research of *** also show the great potential of WRF to perform the multimodal data fusion analysis of other brain diseases.

关键词： multimodal fusion feature Parkinson's disease pathogenic factor detection sample classification weighted random forest model

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Function-structural Interaction with Progressive and Multi-level Feature Fusion for ADHD Classification

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IEEE Journal of Biomedical and Health Informatics 2025年

作者： Cao, Chunhong Li, Xingxing Wang, Mengyang Chen, Xiaolong Gao, Xieping Xiangtan University MOE Key Laboratory of Intelligent Computing and Information Processing Xiangtan411105 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

Individuals with Attention Deficit Hyperactivity Disorder (ADHD) exhibit intricate structural and functional interconnectivity across multiple brain regions. These patients demonstrate abnormal alterations in both respective modal brain regions and co-occurrent brain regions. Furthermore, there exist multi-level relationships between these abnormal brain structures and functions, encompassing hierarchical interactions between function-structural alterations as well as hierarchical progression from local regions to broader brain networks. However, most existing multi-modal ADHD classification approaches independently embed functional and structural data into separate spaces for information integration, often predominately focusing on uni-modal features. This approaches lead to a significant loss of features related to functiona-structural interaction relationships. Additionally, it is crucial for ADHD classification to accurately identify both uni-modal and co-occurrent abnormal alterations in brain regions which have hierarchical progression relationships. This study proposes a function-structural interaction multi-modal network with progressive and multi-level feature fusion (FSIPM) for ADHD classification. The main contributions are threefold: 1) An innovative function-structural interaction method is proposed to facilitate the mutual regulation of information across modalities, thereby relieving modal feature bias caused by integrated fusion. 2) A multi-level refinement framework is designed to promote the identification of both individual and co-occurrent abnormal brain regions. This progressive approach models the function-structural alterations of abnormal brain regions and the hierarchical relationships from local to brain networks, ensuring a deeper understanding of brain abnormalities. 3) Multi-level feature fusion aims to minimize the loss of details caused by consecutive sampling operations during the progressive process of the network, contributing to a

关键词： Modal analysis

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A Faster Parameterized Algorithm for Bipartite 1-Sided Vertex Explosion 16th

A Faster Parameterized Algorithm for Bipartite 1-Sided Vert...

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16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： Liu, Yunlong Xiao, Guang Liu, Ao Wu, Di Huang, Jingui College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China Xiangtan University Xiangtan411101 China School of Computer Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9783031496134

Given a bipartite graph G = (T∪ B, E), the problem bipartite 1-sided vertex explosion is to decide whether there exists a planar 2-layer embedding of G after exploding at most k vertices of B. For this problem, which is known to be NP-complete, parameterized algorithms have received increasing attention more recently. In this paper, we focus on the problem parameterized by the number k of allowed exploded vertices of B and develop a faster algorithm for it. More specifically, we show that this parameterized problem admits a kernel of at most 10.5k vertices, and present a fixed-parameter tractable algorithm running in time O(2. 31 k· m), where m is the number of edges of G. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Embeddings

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Novelty Detection-Based Automated Anomaly Identification via Optimized Deep Generative Model 9th

Novelty Detection-Based Automated Anomaly Identification via...

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9th CCF Conference on Big Data, BigData 2021

作者： Liu, Lianye Liu, Jinping Wu, Juanjuan Zhou, Jiaming Cai, Meiling Hunan Meteorological Science Institute Hunan Changsha410007 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Hunan Changsha410081 China

ISBN: (纸本)9789811697081

Novelty detection (ND) is a crucial task in machine learning to identify anomalies in the test data in some respects different from the training data. As an anomaly detection method, novelty detection only uses normal samples for model learning, which can well fit most of the natural scenes that the amount of abnormal samples is in fact strongly insufficient, such as network intrusion detection, industrial fault detection, and so on, due to the rareness of abnormal events or the high cost of abnormal samples collection. This paper proposes a reconstruction-based ND scheme by introducing an optimized deep generative model (ODGM), which combines the concept of Variational Auto-encoder (VAE) and the generative adversarial network (GAN) model jointly to efficiently and stably learn the essential characteristics from normal samples. A novelty index is established by combining signal reconstruction loss and feature loss between the original signal of the reconstructed signal based on the ODGM on normal samples for anomaly point identification in the test data. The effectiveness and superiority of the proposed model is validated and compared with other representative deep learning-based novelty detection models on two public data sets. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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An Analysis on the Training Mode of Master Students in Artificial Intelligence Field for Electronic Information Professional Degree—Take hunan Normal University as an Example 8th

An Analysis on the Training Mode of Master Students in Artif...

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8th EAI International Conference on e-Learning, e-Education, and Online Training, eLEOT 2022

作者： Fu, Weina Liu, Shuai Institute of Information Science and Engineering Hunan Normal University Hunan Changsha410081 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Changsha410081 China

ISBN: (纸本)9783031211638

The ability of independent innovation in the field of artificial intelligence is a key element to occupy the commanding heights of future technology and talent competition in China. The cultivation of artificial intelligence talents, especially the cultivation high-end talent, it is essential to promote the development of artificial intelligence industry. At present, the market for artificial intelligence talents is in great demand. There is a shortage of high-end artificial intelligence talents, the weak strength of artificial intelligence teachers in universities, and the incomplete education system for talent training and many other issues. This paper combines some measures taken by hunan Normal University for graduate students in the field of artificial intelligence in the degree of electronic information, the necessity and feasibility of artificial intelligence training senior talents are discussed for social needs in the field of artificial intelligence, The training goals and methods of postgraduate students in the field of artificial intelligence under the professional category of electronic information major are clarified, and the construction of artificial intelligence field is planned. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Professional aspects

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