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学者姓名：冀俊忠
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摘要 ：
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.
关键词 ：
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 等. "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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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.
关键词 ：
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 等. "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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
人脑效应连接网络刻画了脑区间神经活动的因果效应.对不同人群的脑效应连接网络进行研究不仅能为神经精神疾病病理机制的理解提供新视角,而且能为疾病的早期诊断和治疗评价提供新的脑网络影像学标记,具有十分重要的理论意义和应用价值.利用计算方法从功能磁共振成像(Functional magnetic resonance imaging,fMRI)数据中识别脑效应连接网络是目前人脑连接组学中一项重要的研究课题.本文首先概括了从fMRI数据中进行脑效应连接网络识别的主要流程,说明了其中的主要步骤和方法;然后,给出了一种脑效应连接网络识别方法的分类体系,并对其中一些代表性的识别算法进行了阐述;最后,通过对该领域...
关键词 ：
功能磁共振成像 功能磁共振成像 脑效应连接网络识别 脑效应连接网络识别 挑战与展望 挑战与展望 分类体系 分类体系 人脑连接组学 人脑连接组学
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | 冀俊忠 , 邹爱笑 , 刘金铎 . 基于功能磁共振成像的人脑效应连接网络识别方法综述 [J]. | 自动化学报 , 2021 , 47 (02) : 278-296 . |
| MLA | 冀俊忠 等. "基于功能磁共振成像的人脑效应连接网络识别方法综述" . | 自动化学报 47 . 02 (2021) : 278-296 . |
| APA | 冀俊忠 , 邹爱笑 , 刘金铎 . 基于功能磁共振成像的人脑效应连接网络识别方法综述 . | 自动化学报 , 2021 , 47 (02) , 278-296 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
一种融合多尺度超像素的脑CT图像分类方法，属于医学图像研究领域。所述方法具有以下特点：1)利用多尺度超像素与脑CT图像融合，去除了图像冗余信息，降低了病灶和周围脑组织像素的灰度相似性。2)设计了一种基于区域和边界的多尺度超像素编码器，有效的提取多尺度超像素中包含的病灶低层次信息。3)设计了一种融合多尺度超像素特征融合模型，综合利用了残差神经网络提取的高层次特征和多尺度超像素的低层次特征，实现对脑CT的分类。4)相比传统深度学习算法，本发明所述方法可以有效利用多尺度超像素中包含的病灶信息，从而更准确地对脑CT图像中存在的疾病进行分类，且该方法合理可靠，可为脑CT图像的分类提供有力的帮助。
引用：
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| GB/T 7714 | 冀俊忠 , 张梦隆 , 张晓丹 . 一种融合多尺度超像素的脑CT图像分类方法 : CN202110058684.0[P]. | 2021-01-16 . |
| MLA | 冀俊忠 等. "一种融合多尺度超像素的脑CT图像分类方法" : CN202110058684.0. | 2021-01-16 . |
| APA | 冀俊忠 , 张梦隆 , 张晓丹 . 一种融合多尺度超像素的脑CT图像分类方法 : CN202110058684.0. | 2021-01-16 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
一种融合多源信息的脑效应连接网络蚁群学习算法
关键词 ：
蚁群算法 蚁群算法 启发函数修正 启发函数修正 脑效应连接网络 脑效应连接网络 搜索空间压缩 搜索空间压缩 多源信息融合 多源信息融合
引用：
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| GB/T 7714 | 冀俊忠 , 刘金铎 , 邹爱笑 et al. 一种融合多源信息的脑效应连接网络蚁群学习算法 [J]. | 冀俊忠 , 2021 , 47 (4) : 864-881 . |
| MLA | 冀俊忠 et al. "一种融合多源信息的脑效应连接网络蚁群学习算法" . | 冀俊忠 47 . 4 (2021) : 864-881 . |
| APA | 冀俊忠 , 刘金铎 , 邹爱笑 , 杨翠翠 , 自动化学报 . 一种融合多源信息的脑效应连接网络蚁群学习算法 . | 冀俊忠 , 2021 , 47 (4) , 864-881 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
基于功能磁共振成像的人脑效应连接网络识别方法综述
关键词 ：
脑效应连接网络识别 脑效应连接网络识别 人脑连接组学 人脑连接组学 分类体系 分类体系 功能磁共振成像 功能磁共振成像 挑战与展望 挑战与展望
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | 冀俊忠 , 邹爱笑 , 刘金铎 et al. 基于功能磁共振成像的人脑效应连接网络识别方法综述 [J]. | 冀俊忠 , 2021 , 47 (2) : 278-296 . |
| MLA | 冀俊忠 et al. "基于功能磁共振成像的人脑效应连接网络识别方法综述" . | 冀俊忠 47 . 2 (2021) : 278-296 . |
| APA | 冀俊忠 , 邹爱笑 , 刘金铎 , 自动化学报 . 基于功能磁共振成像的人脑效应连接网络识别方法综述 . | 冀俊忠 , 2021 , 47 (2) , 278-296 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Attention mechanism has made great progress in image captioning, where semantic words or local regions are selectively embedded into the language model. However, current attention-based image captioning methods ignore the fine-grained semantic information and their interaction with visual regions. Inspired by the activity of human in describing an image: divergent observation and convergent attention, we propose a novel divergent-convergent attention (DCA) model to tackle the problems of the current attention model in image captioning. In our DCA model, divergent observation is mainly reflected in the multi-perspective inputs: a visual collection coming from object detection and three semantic components of scene graph made of objects, attributes and relations respectively. Then the convergent attention merges these multi-perspective inputs by adaptively deciding which perspective is crucial and which element in the focused perspective dominates in the attention process through a hierarchical structure. Our model also makes use of the interaction between visual objects and semantic components to achieve complementary advantages. Above all, owing to the interaction between divergent visual and semantic components, and the gradual convergence of attention, our model can attend to the corresponding local region more precisely under the guidance of semantic components. Besides, with the assistance of the visual components, the DCA model can effectively utilize the fine-grained semantic components to generate more descriptive sentences. Experiments on the MS COCO dataset demonstrate the superiority of our proposed method. (c) 2021 Elsevier Ltd. All rights reserved.
关键词 ：
Convergent Attention Convergent Attention Divergent Observation Divergent Observation Image Captioning Image Captioning
引用：
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| GB/T 7714 | Ji, Junzhong , Du, Zhuoran , Zhang, Xiaodan . Divergent-convergent attention for image captioning [J]. | PATTERN RECOGNITION , 2021 , 115 . |
| MLA | Ji, Junzhong et al. "Divergent-convergent attention for image captioning" . | PATTERN RECOGNITION 115 (2021) . |
| APA | Ji, Junzhong , Du, Zhuoran , Zhang, Xiaodan . Divergent-convergent attention for image captioning . | PATTERN RECOGNITION , 2021 , 115 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
脑效应连接(Effective connectivity, EC)网络是人脑连接组研究中一项重要的研究课题,识别脑效应连接网络已成为评价正常脑功能及其与神经退化疾病相关损伤的一种有效手段.针对从功能性磁共振成像数据中进行脑效应连接网络的学习问题,提出了一种将多源信息与蚁群优化过程相融合的学习方法.新方法首先利用弥散张量成像数据获取感兴趣区域的结构约束信息,并利用正相关的皮尔森信息来压缩蚁群搜索的空间,以避免蚁群的许多不必要的搜索;然后在蚁群随机搜索中通过将体素联合激活信息融合于启发函数中,以增强蚂蚁搜索的目的性,改进算法的优化效率.实验结果验证了所提策略的有效性,与最新的同类算法相比,新算法在保持较快收敛速度的前提下,具有更好的求解质量.
关键词 ：
多源信息融合 多源信息融合 蚁群算法 蚁群算法 启发函数修正 启发函数修正 搜索空间压缩 搜索空间压缩 脑效应连接网络 脑效应连接网络
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | 冀俊忠 , 刘金铎 , 邹爱笑 et al. 一种融合多源信息的脑效应连接网络蚁群学习算法 [J]. | 自动化学报 , 2021 , 47 (4) : 864-881 . |
| MLA | 冀俊忠 et al. "一种融合多源信息的脑效应连接网络蚁群学习算法" . | 自动化学报 47 . 4 (2021) : 864-881 . |
| APA | 冀俊忠 , 刘金铎 , 邹爱笑 , 杨翠翠 . 一种融合多源信息的脑效应连接网络蚁群学习算法 . | 自动化学报 , 2021 , 47 (4) , 864-881 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
提出基于宽度学习系统的功能性磁共振成像(fMRI)数据分类方法,通过简单结构提取fMRI数据的深层特征,加快分类速度.使用fMRI中感兴趣区域体素均值的时间序列构造输入数据,分别提取fMRI数据的浅层和深层特征,映射为宽度学习的特征节点和增强节点并构建模型框架,利用岭回归逆计算分类模型的连接权值,实现对fMRI数据的分类.使用ABIDEⅠ、ABIDEⅡ和ADHD-200数据集,将所提方法与6种分类方法进行对比实验,结果表明,所提方法可以在保持良好的分类准确率的同时,大幅度降低训练时间.
引用：
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| GB/T 7714 | 刘嘉诚 , 冀俊忠 . 基于宽度学习系统的fMRI数据分类方法 [J]. | 浙江大学学报（工学版） , 2021 , 55 (7) : 1270-1278 . |
| MLA | 刘嘉诚 et al. "基于宽度学习系统的fMRI数据分类方法" . | 浙江大学学报（工学版） 55 . 7 (2021) : 1270-1278 . |
| APA | 刘嘉诚 , 冀俊忠 . 基于宽度学习系统的fMRI数据分类方法 . | 浙江大学学报（工学版） , 2021 , 55 (7) , 1270-1278 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Brain network classification has attracted increasing attention with the widespread application in the automatic diagnosis of brain diseases. However, limited by the higher cost of detecting and marking for medical imaging, the amount of brain network data is usually small, which largely restricts the performance of current brain network classification models. In this paper, we propose a new sparse data augmentation model (SDAM) based on EncoderForest to effectively enhance the brain network data and improve the classification performance. The EncoderForest based SDAM uses a generator which innovatively encodes the rules of a set of parallel decision trees to generate sparse data with only discriminative connections. The generated data expands the original data set effectively by utilizing the advantages of EncoderForest in learning data feature sparsely and constructing a feature association generation model compactly. In addition, the SDAM is flexible to combine with different classification models, such as random forest, support vector machine, deep neural network, etc. The experimental results on three common brain disease data sets show that our model is able to reasonably augment the brain network data and remarkably improve the performance of various classifiers.
关键词 ：
Brain network classification Brain network classification EncoderForest EncoderForest Sparse data augmentation Sparse data augmentation
引用：
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| GB/T 7714 | Ji, Junzhong , Wang, Zihan , Zhang, Xiaodan et al. Sparse data augmentation based on encoderforest for brain network classification [J]. | APPLIED INTELLIGENCE , 2021 . |
| MLA | Ji, Junzhong et al. "Sparse data augmentation based on encoderforest for brain network classification" . | APPLIED INTELLIGENCE (2021) . |
| APA | Ji, Junzhong , Wang, Zihan , Zhang, Xiaodan , Li, Junwei . Sparse data augmentation based on encoderforest for brain network classification . | APPLIED INTELLIGENCE , 2021 . |
| 导入链接 | NoteExpress RIS BibTex |
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