Article
Neurosciences
Qiyu Wang, Shijie Zhao, Zhibin He, Shu Zhang, Xi Jiang, Tuo Zhang, Tianming Liu, Cirong Liu, Junwei Han
Summary: The functional differences between gyri and sulci were revisited using a novel neural network model. The results showed functional heterogeneity of cortical folding patterns in different brain networks, which may be contributed by sulci.
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Jing-Wei Liu, Fang-Ling Zuo, Ying-Xiao Guo, Tian-Yue Li, Jia-Ming Chen
Summary: The study proposed wavelet convolutional neural network (wCNN) and wavelet convolutional wavelet neural network (wCwNN), and found that these improved methods increased the complexity of the algorithm while improving both precision and accuracy in image classification experiments.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Mingxin Jiang, Yuzhong Chen, Jiadong Yan, Zhenxiang Xiao, Wei Mao, Boyu Zhao, Shimin Yang, Zhongbo Zhao, Tuo Zhang, Lei Guo, Benjamin Becker, Dezhong Yao, Keith M. Kendrick, Xi Jiang
Summary: This study developed new anatomy-guided spatio-temporal graph convolutional networks (AG-STGCNs) to investigate the regularity and variability of functional connectivity differences between gyri and sulci across multiple task domains. Based on fMRI datasets from the Human Connectome Project, the study found significant differences in functional connectivity between gyral and sulcal regions within task domains compared to resting state. The study also found considerable variability in functional connectivity and information flow between gyri and sulci across different task domains, which are correlated with individual cognitive behaviors.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Xiangyu Zhao, Peng Huang, Xiangbo Shu
Summary: This paper investigates the issues in feature learning methods based on CNN and proposes a new module based on wavelet attention for image classification. Experimental results demonstrate significant improvements in accuracy using this approach.
MULTIMEDIA SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Sudip Modak, Sayanjit Singha Roy, Rohit Bose, Soumya Chatterjee
Summary: In this study, a novel approach for automated detection and classification of focal EEG signals was proposed, utilizing cross wavelet transform and a customized CNN model. The experiment showed promising results, with 100% accuracy achieved for the delta rhythm and significantly reduced training time compared to existing CNN models.
IEEE SENSORS JOURNAL
(2021)
Article
Engineering, Mechanical
Huan Wang, Zhiliang Liu, Dandan Peng, Ming J. Zuo
Summary: This paper proposes a multilayer wavelet attention convolutional neural network (MWA-CNN) for noise-robust machinery fault diagnosis. The framework aims to learn discriminative fault features from the wavelet domain, which allows the model to obtain better interpretability and superior performance than conventional time-domain-based CNNs. Experiments on high-speed aeronautical bearing and motor bearing datasets prove that the proposed method has excellent fault diagnosis ability and noise robustness, and the visual analysis of the attention mechanism contributes to the interpretability of CNN in the field of fault diagnosis.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Chaorong Li, Yuanyuan Huang, Wei Huang, Fengqing Qin
Summary: This study proposes two promising methods (LCMoG) for improving face recognition performance by learning the covariance matrix of Gabor wavelet. One method uses a shallow Convolutional Neural Network to project covariance matrices into Euclidean space, while the other method embeds the covariance matrix in linear space using matrix logarithm and learns face features through Whitening Principal Component Analysis.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Juntong Yun, Du Jiang, Li Huang, Bo Tao, Shangchun Liao, Ying Liu, Xin Liu, Gongfa Li, Disi Chen, Baojia Chen
Summary: In this study, a dual manipulator grasping detection model based on the Markov decision process is proposed. By parameterizing the grasping detection model of dual manipulators using a cross entropy convolutional neural network and a full convolutional neural network, stable grasping of complex multiple objects is achieved. Robot grasping experiments were conducted to verify the feasibility and superiority of this method.
Article
Chemistry, Analytical
Muhammad Fayaz, Nurlan Torokeldiev, Samat Turdumamatov, Muhammad Shuaib Qureshi, Muhammad Bilal Qureshi, Jeonghwan Gwak
Summary: This paper introduces a brain MR image classification model based on discrete wavelet transform and convolutional neural network, which consists of three main stages: preprocessing, feature extraction, and classification. The proposed model has shown good results with high accuracy, outperforming other algorithms in comparison.
Article
Chemistry, Analytical
Feng He, Qing Ye
Summary: A new method for bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by a simulated annealing algorithm is proposed, showing better and more reliable diagnosis effect compared to existing machine learning and deep learning methods.
Article
Agronomy
Qiang Cui, Baohua Yang, Biyun Liu, Yunlong Li, Jingming Ning
Summary: This research proposes a method for tea recognition based on a lightweight convolutional neural network and support vector machine, utilizing wavelet feature figures. The results demonstrate that this method outperforms other techniques, achieving an accuracy rate of 98.7%. This study has important practical significance for the grading and quality assessment of tea.
Review
Agriculture, Multidisciplinary
Daoliang Li, Zhaoyang Song, Chaoqun Quan, Xianbao Xu, Chang Liu
Summary: This article discusses the importance of crop and livestock monitoring in agricultural production, as well as the application of image fusion technology in improving monitoring methods. It reviews the specific applications of image fusion in areas such as crop recognition, disease detection, and livestock health assessment, while also highlighting the challenges and future research directions in the field.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Chemistry, Multidisciplinary
Jinhwan Yoo, Chang-Hun Lee, Hae-Min Jea, Sang-Kwon Lee, Youngsam Yoon, Jaehun Lee, Kiho Yum, Seoung-Uk Hwang
Summary: This paper presents a novel approach for road surface classification using deep learning method-based CNN architecture. Traditional methods using accelerometers and vision sensors have some limitations. This study adopts TPIN as a data source and uses a CNN architecture for classification, and the results show that this method is feasible.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Multidisciplinary
Mantang Hu, Guofeng Wang, Kaile Ma, Zenghuan Cao, Shuai Yang
Summary: A method for bearing performance degradation assessment is proposed, using optimized empirical wavelet transform and fuzzy C-means model to improve the sensitivity and stability of the assessment method in extracting fault information.
Article
Anatomy & Morphology
Xiao Li, Songyao Zhang, Xi Jiang, Shu Zhang, Junwei Han, Lei Guo, Tuo Zhang
Summary: Postnatal development of the cerebral cortex is crucial for brain function and cognition. This study investigated the longitudinal changes of cortical morphology and topology during early development using a macaque neuroimaging dataset. The results showed that there are four classes of regions based on the changes in surface area and sulcal depth: slowA_slowD, slowA_fastD, fastA_slowD, and fastA_fastD. The study also found correlations between cortical metrics, structural connections, and brain functional sites.
BRAIN STRUCTURE & FUNCTION
(2022)
Article
Neurosciences
Zhibin He, Lei Du, Ying Huang, Xi Jiang, Jinglei Lv, Lei Guo, Shu Zhang, Tuo Zhang
Summary: Previous studies have reported the small-world and rich-world attributes of the global structure of brain networks, but the relationship between these structural and functional characteristics and cortical morphology has not been explicitly studied. By introducing a new folding pattern called the gyral hinge (GH), which combines ordinary gyri from multiple directions, this study found that GHs possess the highest length and cost in the white matter fiber connective network, and that the shortest paths in the network tend to pass through GHs in their middle part. Based on these findings, the authors hypothesize that GHs could be located at the centers of a network core, thus accounting for the highest cost and communication capacity in a corticocortical network.
Article
Neurosciences
Lu Zhang, Lin Zhao, David Liu, Zihao Wu, Xianqiao Wang, Tianming Liu, Dajiang Zhu
Summary: Current brain mapping methods often overlook individual structural information. We propose a new cortical folding pattern called 3-hinge gyrus (3HG) that can encode both commonality and individuality, and infer anatomical correspondences among different brains.
Article
Computer Science, Artificial Intelligence
Xi Jiang, Jiadong Yan, Yu Zhao, Mingxin Jiang, Yuzhong Chen, Jingchao Zhou, Zhenxiang Xiao, Zifan Wang, Rong Zhang, Benjamin Becker, Dajiang Zhu, Keith M. Kendrick, Tianming Liu
Summary: In this study, a novel Spatio-Temporal Attention 4D Convolutional Neural Network (STA-4DCNN) model is introduced to characterize individualized spatio-temporal patterns of functional brain networks (FBNs). The experimental results show that STA-4DCNN has superior ability in characterizing FBN patterns and can effectively distinguish abnormal patterns in brain disorders. This study provides a powerful tool for FBN characterization and clinical applications.
Article
Computer Science, Interdisciplinary Applications
Chong Ma, Lin Zhao, Yuzhong Chen, Sheng Wang, Lei Guo, Tuo Zhang, Dinggang Shen, Xi Jiang, Tianming Liu
Summary: This paper introduces a novel eye-gaze-guided vision transformer (EG-ViT) model to rectify harmful shortcuts in medical image analysis and improve model interpretability. Experimental results demonstrate the effectiveness of the proposed model and the performance improvement achieved by infusing domain knowledge.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Computer Science, Artificial Intelligence
Lin Zhao, Zihao Wu, Haixing Dai, Zhengliang Liu, Xintao Hu, Tuo Zhang, Dajiang Zhu, Tianming Liu
Summary: In this study, a novel embedding framework based on the Transformer model is proposed to represent human brain function from high-dimensional fMRI data. The framework encodes brain activities as dense embedding vectors in a compact, stereotyped, and comparable latent space. The results show the effectiveness and generalizability of the learned embedding for brain state prediction tasks and provide new insights into representing the regularity and variability of human brain function.
MEDICAL IMAGE ANALYSIS
(2023)
Article
Neurosciences
Liting Wang, Xintao Hu, Yudan Ren, Jinglei Lv, Shijie Zhao, Lei Guo, Tianming Liu, Junwei Han
Summary: This study investigates the effective amygdala-insula interactions and the modulatory effect of arousal on the connections between these two regions. The results demonstrate that the amygdala is the driving force behind arousal and that arousal has a modulatory effect on the reciprocal connections between the amygdala and insula. These findings provide novel insights into the underlying neural mechanisms of arousal in a dynamic naturalistic setting.
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Shengjie Zhang, Xiang Chen, Xin Shen, Bohan Ren, Ziqi Yu, Haibo Yang, Xi Jiang, Dinggang Shen, Yuan Zhou, Xiao-Yong Zhang
Summary: This study proposes an adversarial self-supervised graph neural network (A-GCL) for diagnosing neurodevelopmental disorders using fMRI data. The A-GCL model shows superior performance and generalizability compared to other GNN-based models, and reveals key functional connections and brain regions associated with neurodevelopmental disorders.
MEDICAL IMAGE ANALYSIS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Saed Rezayi, Haixing Dai, Zhengliang Liu, Zihao Wu, Akarsh Hebbar, Andrew H. Burns, Lin Zhao, Dajiang Zhu, Quanzheng Li, Wei Liu, Sheng Li, Tianming Liu, Xiang Li
Summary: This paper presents ClinicalRadioBERT, a BERT-based model for analyzing clinical notes. By pretraining and fine-tuning on radiotherapy literature, as well as incorporating knowledge-infused few-shot learning, the model demonstrates superior performance in few-shot named entity recognition.
MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2022
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Haixing Dai, Qing Li, Lin Zhao, Liming Pan, Cheng Shi, Zhengliang Liu, Zihao Wu, Lu Zhang, Shijie Zhao, Xia Wu, Tianming Liu, Dajiang Zhu
Summary: A graph representation neural architecture search method is proposed in this paper to optimize the RNN cell structure for decomposing spatial/temporal brain networks from fMRI data.
MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2022
(2022)
Article
Computer Science, Artificial Intelligence
Mingxin Jiang, Yuzhong Chen, Jiadong Yan, Zhenxiang Xiao, Wei Mao, Boyu Zhao, Shimin Yang, Zhongbo Zhao, Tuo Zhang, Lei Guo, Benjamin Becker, Dezhong Yao, Keith M. Kendrick, Xi Jiang
Summary: This study developed new anatomy-guided spatio-temporal graph convolutional networks (AG-STGCNs) to investigate the regularity and variability of functional connectivity differences between gyri and sulci across multiple task domains. Based on fMRI datasets from the Human Connectome Project, the study found significant differences in functional connectivity between gyral and sulcal regions within task domains compared to resting state. The study also found considerable variability in functional connectivity and information flow between gyri and sulci across different task domains, which are correlated with individual cognitive behaviors.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yuzhong Chen, Jiadong Yan, Mingxin Jiang, Tuo Zhang, Zhongbo Zhao, Weihua Zhao, Jian Zheng, Dezhong Yao, Rong Zhang, Keith M. Kendrick, Xi Jiang
Summary: This study proposes an adversarial learning-based node-edge graph attention network (AL-NEGAT) for the identification of autism spectrum disorder (ASD) using multimodal MRI data. The AL-NEGAT model leverages both node and edge features to improve classification accuracy and generalizability. Experimental results demonstrate the effectiveness of the proposed framework in ASD classification.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)