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学者姓名：冀俊忠
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Abstract ：
In recent years, the discovery of brain effective connectivity (EC) networks through computational analysis of functional magnetic resonance imaging (fMRI) data has gained prominence in neuroscience and neuroimaging. However, owing to the influence of diverse factors during data collection and processing, fMRI data typically exhibit high noise and limited sample characteristics, consequently leading to the suboptimal performance of current methods. In this paper, we propose a novel brain effective connectivity discovery method based on meta-reinforcement learning, called MetaRLEC. The method mainly consists of three modules: actor, critic, and meta-critic. MetaRLEC first employs an encoder-decoder framework: The encoder utilizing a transformer converts noisy fMRI data into a state embedding, and the decoder employing bidirectional LSTM discovers brain region dependencies from the state and generates actions (EC networks). Then, a critic network evaluates these actions, incentivizing the actor to learn higher-reward actions amidst the high-noise setting. Finally, a meta-critic framework facilitates online learning of historical state-action pairs, integrating an action-value neural network and supplementary training losses to enhance the model's adaptability to small-sample fMRI data. We conduct comprehensive experiments on both simulated and real-world data to demonstrate the efficacy of our proposed method.
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| GB/T 7714 | Zhang, Zuozhen , Ji, Junzhong , Liu, Jinduo . MetaRLEC: Meta-Reinforcement Learning for Discovery of Brain Effective Connectivity [J]. | THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 9 , 2024 : 10261-10269 . |
| MLA | Zhang, Zuozhen 等. "MetaRLEC: Meta-Reinforcement Learning for Discovery of Brain Effective Connectivity" . | THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 9 (2024) : 10261-10269 . |
| APA | Zhang, Zuozhen , Ji, Junzhong , Liu, Jinduo . MetaRLEC: Meta-Reinforcement Learning for Discovery of Brain Effective Connectivity . | THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 9 , 2024 , 10261-10269 . |
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Abstract ：
近年来,基于深度学习的脑功能连接分类方法成为脑科学中的研究热点。为了进一步获得具有判别性的大脑连接特征,进而提升脑功能连接的分类准确率,本文提出了一种基于对比学习的卷积神经网络脑功能连接分类方法。对比学习是一种特殊的自监督学习框架,通过在特征空间中对正样本与负样本进行对比,充分挖掘不同样本间的差异性。首先,提出一种融合多视角信息的对比学习框架,采用实例-实例的对比学习挖掘样本间的差异性,并利用实例-原型的对比学习挖掘样本与样本簇之间的差异性。其次,将多视角对比学习与目标分类任务联合训练,增强所学特征的判别性,提升卷积神经网络的分类性能。本文使用ABIDE-I数据集进行实验,结果表明,所提方法能够使脑功能连接的分类效果得到有效提升,并且能够对重要的功能连接与脑区进行准确识别。
Keyword ：
对比学习 对比学习 脑功能连接分类 脑功能连接分类 卷积神经网络 卷积神经网络
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| GB/T 7714 | 何庆钊 , 于乐 , 冀俊忠 et al. 基于对比学习的卷积神经网络脑功能连接分类方法 [C] //2023中国自动化大会 . 2023 . |
| MLA | 何庆钊 et al. "基于对比学习的卷积神经网络脑功能连接分类方法" 2023中国自动化大会 . (2023) . |
| APA | 何庆钊 , 于乐 , 冀俊忠 , 雷名龙 . 基于对比学习的卷积神经网络脑功能连接分类方法 2023中国自动化大会 . (2023) . |
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Abstract ：
Deep forest is a new multi-layer ensemble model, where the high time costs and storage requirements inhibit its large-scale application. However, current deep forest pruning methods used to alleviate these drawbacks do not consider its cascade coupling characteristics. Therefore, we propose a tri-objective optimization-based cascade ensemble pruning (TOOCEP) algorithm for it. Concretely, we first present a tri-objective optimization-based single-layer pruning (TOOSLP) method to prune its single-layer by simultaneously optimizing three objectives, namely accuracy, independent diversity, and coupled diversity. Particularly, the coupled diversity is designed for deep forest to deal with the coupling relationships between its adjacent layers. Then, we perform TOOSLP in a cascade framework to prune the deep forest layer-by-layer. Experimental results on 15 UCI datasets show that TOOCEP outperforms several state-ofthe-art methods in accuracy and pruned rate, which significantly reduces the storage space and accelerate the prediction speed of deep forest. & COPY; 2023 Elsevier Ltd. All rights reserved.
Keyword ：
Deep forest Deep forest Ensemble pruning Ensemble pruning Ensemble learning Ensemble learning Coupled diversity Coupled diversity Multi-objective optimization Multi-objective optimization
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| GB/T 7714 | Ji, Junzhong , Li, Junwei . Tri-objective optimization-based cascade ensemble pruning for deep forest [J]. | PATTERN RECOGNITION , 2023 , 143 . |
| MLA | Ji, Junzhong et al. "Tri-objective optimization-based cascade ensemble pruning for deep forest" . | PATTERN RECOGNITION 143 (2023) . |
| APA | Ji, Junzhong , Li, Junwei . Tri-objective optimization-based cascade ensemble pruning for deep forest . | PATTERN RECOGNITION , 2023 , 143 . |
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Abstract ：
Using machine learning methods to estimate brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data has garnered significant attention in the fields of neuroinformatics and bioinformatics. However, existing methods usually require retraining the model for each subject, which ignores the knowledge shared across subjects. In this paper, we propose a novel framework for estimating effective connectivity based on an amortization transformer, named AT-EC. In detail, AT-EC first employs an amortization transformer to model the dynamics of fMRI time series and infer brain effective connectivity across different subjects, which can train an amortized model that leverages the shared knowledge from different subjects. Then, an assisted learning mechanism based on functional connectivity is designed to assist the estimation of the brain effective connectivity network. Experimental results on both simulated and real-world data demonstrate the efficacy of our method.
Keyword ：
brain effective connectivity brain effective connectivity transformer transformer functional magnetic resonance imaging functional magnetic resonance imaging amortization learning amortization learning
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| GB/T 7714 | Zhang, Zuozhen , Zhang, Ziqi , Ji, Junzhong et al. Amortization Transformer for Brain Effective Connectivity Estimation from fMRI Data [J]. | BRAIN SCIENCES , 2023 , 13 (7) . |
| MLA | Zhang, Zuozhen et al. "Amortization Transformer for Brain Effective Connectivity Estimation from fMRI Data" . | BRAIN SCIENCES 13 . 7 (2023) . |
| APA | Zhang, Zuozhen , Zhang, Ziqi , Ji, Junzhong , Liu, Jinduo . Amortization Transformer for Brain Effective Connectivity Estimation from fMRI Data . | BRAIN SCIENCES , 2023 , 13 (7) . |
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Abstract ：
A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data.
Keyword ：
causal protein signaling networks causal protein signaling networks CBNs fusion CBNs fusion causal biological networks causal biological networks parallel ant colony optimization parallel ant colony optimization pheromone fusion pheromone fusion causal brain networks causal brain networks
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| GB/T 7714 | Zhai, Jihao , Ji, Junzhong , Liu, Jinduo . Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm [J]. | BIOENGINEERING-BASEL , 2023 , 10 (8) . |
| MLA | Zhai, Jihao et al. "Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm" . | BIOENGINEERING-BASEL 10 . 8 (2023) . |
| APA | Zhai, Jihao , Ji, Junzhong , Liu, Jinduo . Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm . | BIOENGINEERING-BASEL , 2023 , 10 (8) . |
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Abstract ：
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.
Keyword ：
Semantics Semantics Codes Codes Brain modeling Brain modeling Brain network classification Brain network classification proximity relationships proximity relationships Feature extraction Feature extraction deep graph hashing learning deep graph hashing learning Hash functions Hash functions Binary codes Binary codes semantic space semantic space Diseases Diseases
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| GB/T 7714 | Ji, Junzhong , Zhang, Yaqin . Functional Brain Network Classification Based on Deep Graph Hashing Learning [J]. | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2022 , 41 (10) : 2891-2902 . |
| MLA | Ji, Junzhong et al. "Functional Brain Network Classification Based on Deep Graph Hashing Learning" . | IEEE TRANSACTIONS ON MEDICAL IMAGING 41 . 10 (2022) : 2891-2902 . |
| APA | Ji, Junzhong , Zhang, Yaqin . Functional Brain Network Classification Based on Deep Graph Hashing Learning . | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2022 , 41 (10) , 2891-2902 . |
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Abstract ：
Spatiotemporal graph neural networks (GNNs) have been used successfully in traffic prediction in recent years, primarily owing to their ability to model complex spatiotemporal dependencies within irregular traffic networks. However, the feature extraction processes in these methods are limited in their exploration of the inner properties of traffic data. Specifically, graph and temporal convolutions are local operations and can hardly utilize information from wider ranges, which may affect the long-term prediction performance of such methods. Furthermore, deep spatiotemporal GNNs easily suffer from poor generalization owing to overfitting. To address these problems, this study presents a novel traffic prediction method that integrates self-supervised learning and self-distillation into spatiotemporal GNNs. First, a self-supervised learning module is used to explore the knowledge from the input data. An auxiliary task based on temporal continuity is designed to capture the contextual information in traffic data. Second, a self-distillation framework is developed as an implicit regularization approach that transfers knowledge from the model itself. The combination of self-supervision and self-distillation further mines the knowledge from the data and the model, and the generalization ability and stability of the prediction model can be improved. The proposed model achieved superior or competitive results compared with several strong baselines on six traffic prediction datasets. In particular, the maximum performance improvement ratios for the six datasets were 3.0% (MAE), 5.2% (RMSE), and 3.8% (MAPE). These results demonstrate the effectiveness of the proposed method.
Keyword ：
Traffic prediction Traffic prediction Predictive models Predictive models Task analysis Task analysis self-distillation self-distillation Data models Data models Spatiotemporal phenomena Spatiotemporal phenomena graph neural network graph neural network Self-supervised learning Self-supervised learning Feature extraction Feature extraction Data mining Data mining self-supervised learning self-supervised learning
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| GB/T 7714 | Ji, Junzhong , Yu, Fan , Lei, Minglong . Self-Supervised Spatiotemporal Graph Neural Networks With Self-Distillation for Traffic Prediction [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2022 , 24 (2) : 1580-1593 . |
| MLA | Ji, Junzhong et al. "Self-Supervised Spatiotemporal Graph Neural Networks With Self-Distillation for Traffic Prediction" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 24 . 2 (2022) : 1580-1593 . |
| APA | Ji, Junzhong , Yu, Fan , Lei, Minglong . Self-Supervised Spatiotemporal Graph Neural Networks With Self-Distillation for Traffic Prediction . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2022 , 24 (2) , 1580-1593 . |
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Abstract ：
Multiobjective evolutionary algorithms (MOEAs) have received much attention in multiobjective optimization in recent years due to their practicality. With limited computational resources, most existing MOEAs cannot efficiently solve large-scale multiobjective optimization problems (LSMOPs) that widely exist in the real world. This paper innovatively proposes a dual decomposition strategy (DDS) that can be embedded into many existing MOEAs to improve their performance in solving LSMOPs. Firstly, the outer decomposition uses a sliding window to divide large-scale decision variables into overlapped subsets of small-scale ones. A small-scale multiobjective optimization problem (MOP) is generated every time the sliding window slides. Then, once a small-scale MOP is generated, the inner decomposition immediately creates a set of global direction vectors to transform it into a set of single-objective optimization problems (SOPs). At last, all SOPs are optimized by adopting a block coordinate descent strategy, ensuring the solution's integrity and improving the algorithm's performance to some extent. Comparative experiments on benchmark test problems with seven state-of-the-art evolutionary algorithms and a deep learning-based algorithm framework have shown the remarkable efficiency and solution quality of the proposed DDS. Meanwhile, experiments on two real-world problems show that DDS can achieve the best performance beyond at least one order of magnitude with up to 3072 decision variables.
Keyword ：
Decomposition Decomposition Block coordinate descent Block coordinate descent Large-scale multiobjective optimization Large-scale multiobjective optimization Sliding window Sliding window
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| GB/T 7714 | Yang, Cuicui , Wang, Peike , Ji, Junzhong . A dual decomposition strategy for large-scale multiobjective evolutionary optimization [J]. | NEURAL COMPUTING & APPLICATIONS , 2022 , 35 (5) : 3767-3788 . |
| MLA | Yang, Cuicui et al. "A dual decomposition strategy for large-scale multiobjective evolutionary optimization" . | NEURAL COMPUTING & APPLICATIONS 35 . 5 (2022) : 3767-3788 . |
| APA | Yang, Cuicui , Wang, Peike , Ji, Junzhong . A dual decomposition strategy for large-scale multiobjective evolutionary optimization . | NEURAL COMPUTING & APPLICATIONS , 2022 , 35 (5) , 3767-3788 . |
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Abstract ：
Classification of dynamic functional connectivity (DFC) is becoming a promising approach for diagnosing various neurodegenerative diseases. However, the existing methods generally face the problem of overfitting. To solve it, this paper proposes a convolutional neural network with three sparse strategies named SCNN to classify DFC. Firstly, an element-wise filter is designed to impose sparse constraints on the DFC matrix by replacing the redundant elements with zeroes, where the DFC matrix is specially constructed to quantify the spatial and temporal variation of DFC. Secondly, a 1x1 convolutional filter is adopted to reduce the dimensionality of the sparse DFC matrix, and remove meaningless features resulted from zero elements in the subsequent convolution process. Finally, an extra sparse optimization classifier is employed to optimize the parameters of the above two filters, which can effectively improve the ability of SCNN to extract discriminative features. Experimental results on multiple resting-state fMRI datasets demonstrate that the proposed model provides a better classification performance of DFC compared with several state-of-the-art methods, and can identify the abnormal brain functional connectivity.
Keyword ：
Symmetric matrices Symmetric matrices feature extraction feature extraction Functional magnetic resonance imaging Functional magnetic resonance imaging Convolutional neural network Convolutional neural network resting-state functional Magnetic Resonance Imaging (rs-fMRI) resting-state functional Magnetic Resonance Imaging (rs-fMRI) Windows Windows Feature extraction Feature extraction Diseases Diseases Sparse matrices Sparse matrices Time series analysis Time series analysis sparse strategies sparse strategies dynamic functional connectivity dynamic functional connectivity
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| GB/T 7714 | Ji, Junzhong , Chen, Zhihui , Yang, Cuicui . Convolutional Neural Network With Sparse Strategies to Classify Dynamic Functional Connectivity [J]. | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS , 2022 , 26 (3) : 1219-1228 . |
| MLA | Ji, Junzhong et al. "Convolutional Neural Network With Sparse Strategies to Classify Dynamic Functional Connectivity" . | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 26 . 3 (2022) : 1219-1228 . |
| APA | Ji, Junzhong , Chen, Zhihui , Yang, Cuicui . Convolutional Neural Network With Sparse Strategies to Classify Dynamic Functional Connectivity . | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS , 2022 , 26 (3) , 1219-1228 . |
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Abstract ：
Recently, functional brain network analysis via graph neural networks has achieved state-of-the-art results as it can directly extract information from irregular graphs without any approximation. However, current methods remain limited in exploring the high-order structural information of brain networks. To address this issue, we propose a hypergraph attention network for functional brain network classification (FC-HAT). First, we build a dynamic hypergraph generation phase and model each brain network as a hypergraph to preserve the high-order information. The pair-wise and community-wise similarities in functional brain networks are separately captured by k nearest neighbors and k-means. Theoretical analysis shows that the constructed hypergraph exhibits superior spectral properties. Then, we design a hypergraph attention aggregation phase to further extract information in hypergraphs. This includes node and hyperedge attention layers that can separately aggregate features among nodes and hyperedges. Finally, the two phases are jointly optimized in an end-to-end manner, which can dynamically update hypergraphs and node embeddings along with the training process. Experimental results on ABIDE-I and ADHD-200 demonstrate the effectiveness of FC-HAT in cerebral disease classification. Moreover, the abnormal connectivity patterns and brain regions identified by FC-HAT are found to be more likely to become biomarkers associated with cerebral diseases.(c) 2022 Elsevier Inc. All rights reserved.
Keyword ：
Graph Neural Networks Graph Neural Networks Functional Brain Networks Functional Brain Networks Hypergraphs Hypergraphs Attention Mechanism Attention Mechanism
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| GB/T 7714 | Ji, Junzhong , Ren, Yating , Lei, Minglong . FC-HAT: Hypergraph attention network for functional brain network classification [J]. | INFORMATION SCIENCES , 2022 , 608 : 1301-1316 . |
| MLA | Ji, Junzhong et al. "FC-HAT: Hypergraph attention network for functional brain network classification" . | INFORMATION SCIENCES 608 (2022) : 1301-1316 . |
| APA | Ji, Junzhong , Ren, Yating , Lei, Minglong . FC-HAT: Hypergraph attention network for functional brain network classification . | INFORMATION SCIENCES , 2022 , 608 , 1301-1316 . |
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