您的检索：
学者姓名：冀俊忠
精炼检索结果：
年份
成果类型
收录类型
来源
综合
合作者
语言
清除所有精炼条件
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：
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 , 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 ：
Detecting clusters over attributed graphs is a fundamental task in the graph analysis field. The goal is to partition nodes into dense clusters based on both their attributes and structures. Modern graph neural networks provide facilitation to jointly capture the above information in attributed graphs with a feature aggregation manner, and have achieved great success in attributed graph clustering. However, existing methods mainly focus on capturing the proximity information in graphs and often fail to learn cluster-friendly features during the training of models. Besides, similar to many deep clustering frameworks, current methods based on graph neural networks require a preassigned cluster number before estimating the clusters. To address these limitations, we propose in this paper a deep attributed clustering method based on self-separated graph neural networks and parameter-free cluster estimation. First, to learn cluster-friendly features, we jointly optimize a jumping graph convolutional auto-encoder with a self-separation regularizer, which learns clusters with changing sizes while keeping dense intra-cluster structures and sparse inter structures. Second, an additional softmax auto-encoder is trained to determine the natural cluster number from the data. The hidden units capture cluster structures and can be used to estimate the number of clusters. Extensive experiments show the effectiveness of the proposed model. (C) 2021 Elsevier Ltd. All rights reserved.
关键词 ：
Attributed graph clustering Attributed graph clustering Graph convolutional networks Graph convolutional networks Parameter-free cluster estimation Parameter-free cluster estimation Self-separation regularization Self-separation regularization
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 | Ji, Junzhong , Liang, Ye , Lei, Minglong . Deep attributed graph clustering with self-separation regularization and parameter-free cluster estimation [J]. | NEURAL NETWORKS , 2021 , 142 : 522-533 . |
| MLA | Ji, Junzhong 等. "Deep attributed graph clustering with self-separation regularization and parameter-free cluster estimation" . | NEURAL NETWORKS 142 (2021) : 522-533 . |
| APA | Ji, Junzhong , Liang, Ye , Lei, Minglong . Deep attributed graph clustering with self-separation regularization and parameter-free cluster estimation . | NEURAL NETWORKS , 2021 , 142 , 522-533 . |
| 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：
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]. | 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 ：
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 ：
启发函数修正 启发函数修正 多源信息融合 多源信息融合 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 蚁群算法 蚁群算法
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 ：
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：
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 | 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 |
摘要 ：
Brain disease diagnosis based on brain network classification has become a hot topic. Recently, classification methods based on convolutional neural networks (CNNs) have attracted much attention due to their ability to capture the basic topological structure of the brain network. However, they ignore abnormal structures within modules caused by brain disease, which limits the diagnostic accuracy. In this paper, we propose a novel brain network classification framework based on a CNN model capable of extracting modular features from brain networks at the node and whole-network levels. More specifically, we first develop a novel algorithm to obtain the modular structure of each node, which is then fed into a CNN model to extract the node-level modular features. Second, we minimize the harmonic modularity of the extracted node-level features to reveal the modular structure at the whole-brain network level. Finally, we employ a deep neural network to further extract high-level features for the classification of brain disease. The experimental results on a real-world autism spectrum disorder dataset show that our proposed method achieves the best accuracy of 68.55% and outperforms other common methods and demonstrates the discriminant power of the modular features at multiple levels. In addition, feature analysis based on the trained framework reveals the associations between modular structures and brain disease, which provides new insights into the pathological mechanism from the perspective of modular structures.
关键词 ：
Brain disease Brain disease Convolutional neural network Convolutional neural network Functional connectivity Functional connectivity Harmonic modularity Harmonic modularity Modular features Modular features
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 | Ji, Junzhong , Yao, Yao . A novel CNN framework to extract multi-level modular features for the classification of brain networks [J]. | APPLIED INTELLIGENCE , 2021 . |
| MLA | Ji, Junzhong et al. "A novel CNN framework to extract multi-level modular features for the classification of brain networks" . | APPLIED INTELLIGENCE (2021) . |
| APA | Ji, Junzhong , Yao, Yao . A novel CNN framework to extract multi-level modular features for the classification of brain networks . | APPLIED INTELLIGENCE , 2021 . |
| 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：
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 | 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 ：
Deep learning based human brain network classification has gained increasing attention in recent years. However, current methods remain limited in exploring the topological structure information of a brain network. In this paper, we propose a kind of new convolutional kernels with an element-wise weighting mechanism (CKEW) to extract hierarchical topological features of brain networks, in which each weight is assigned to an element with a unique neuroscientific meaning. In addition, a novel classification framework based on CKEW is presented to diagnose brain diseases and explore the most important original features by a tracing feature analysis method efficiently. Experimental results on two autism spectrum disorder (ASD) datasets and an attention deficit hyperactivity disorder (ADHD) dataset with functional magnetic resonance imaging (fMRI) data demonstrate that our method can more accurately distinguish subject groups compared to several state-of-the-art methods in cerebral disease classification, and abnormal connectivity patterns and brain regions identified are more likely to become biomarkers associated with a cerebral disease. (C) 2020 Elsevier Ltd. All rights reserved.
关键词 ：
Abnormal connectivity patterns Abnormal connectivity patterns Brain network classification Brain network classification Convolutional kernels Convolutional kernels Element-wise weighting mechanism Element-wise weighting mechanism Topological features Topological features
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 | Ji, Junzhong , Xing, Xinying , Yao, Yao et al. Convolutional kernels with an element-wise weighting mechanism for identifying abnormal brain connectivity patterns [J]. | PATTERN RECOGNITION , 2021 , 109 . |
| MLA | Ji, Junzhong et al. "Convolutional kernels with an element-wise weighting mechanism for identifying abnormal brain connectivity patterns" . | PATTERN RECOGNITION 109 (2021) . |
| APA | Ji, Junzhong , Xing, Xinying , Yao, Yao , Li, Junwei , Zhang, Xiaodan . Convolutional kernels with an element-wise weighting mechanism for identifying abnormal brain connectivity patterns . | PATTERN RECOGNITION , 2021 , 109 . |
| 导入链接 | NoteExpress RIS BibTex |
导出
| 数据： |
选中 到 |
| 格式： |
