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IEEE International Conference on Information Communication and Signal processing (ICICSP)

作者： Chang Yao Menghan Hu Qingli Li Guangtao Zhai Xiao-Ping Zhang Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China Department of Electrical Computer and Biomedical Engineering Ryerson University Toronto Canada

In medical image analysis, the long-range spatial features are often not accurately obtained by the traditional convolutional neural networks. Hence, we propose a TransClaw U-Net network structure. The transformer part is added after three convolution operations to fuse shallow features extracted by convolution operations for maximally encoding the long-range spatial features between patches. The “Claw” in TransClaw U-net means that we add the bottom upsampling part to retain the deepest feature information for detail segmentation. In addition, the modified three-channel global attention mechanism to blend the outputs of three channels (the encoding part, the bottom upsampling part and the decoding part) to effectively extract image contours. The experimental results on Synapse Multi-organ Segmentation Dataset show that TransClaw U-Net performs better than other networks. The results of ablation experiments prove the effectiveness of the three improved components of the network and influence of input image size and skip-connection numbers on network performance. The source code will be publicly available once the paper is accepted.

关键词： Measurement image segmentation image coding Convolution Fuses Source coding Transformers

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Effects of spatially dense adrenergic stimulation to rotor behaviour in simulated atrial sheets

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computers in Biology and Medicine 2024年 182卷 109195-109195页

作者： Magtibay, Karl Massé, Stéphane Nanthakumar, Kumaraswamy Umapathy, Karthikeyan Biomedical Signal and Image Processing Laboratory Department of Electrical Computer and Biomedical Engineering Faculty of Engineering and Architectural Science Toronto Metropolitan University 350 Victoria St TorontoONM5B 2K3 Canada Toby Hull Cardiac Fibrillation Management Toronto General Hospital University Health Network 200 Elizabeth Street TorontoONM5G 2C4 Canada

Sympathetic hyperactivity via spatially dense adrenergic stimulation may create pro-arrhythmic substrates even without structural remodelling. However, the effect of sympathetic hyperactivity on arrhythmic activity, such as rotors, is unknown. Using simulations, we examined the effects of gradually increasing the spatial density of adrenergic stimulation (AS) in atrial sheets on rotors. We compared their characteristics against rotors hosted in atrial sheets with increasing spatial density of minimally conductive (MC) elements to simulate structural remodelling due to injury or disease. We generated rotors using an S1-S2 stimulation protocol. Then, we created phase maps to identify phase singularities and map their trajectory over time. We measured each rotor's duration (s), angular speed (rad/s), and spatiotemporal organization. We demonstrated that atrial sheets with increased AS spatial densities could maintain rotors longer than with MC elements (2.6 ± 0.1 s vs. 1.5 ± 0.2 s, p © 2024 The Authors

关键词： Digital elevation model

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An Insight Into Neurodegeneration: Harnessing Functional MRI Connectivity in the Diagnosis of Mild Cognitive Impairment

An Insight Into Neurodegeneration: Harnessing Functional MRI...

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17th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Han, Shuning Sun, Zhe Zhao, Kanhao Duan, Feng Caiafa, Cesar F. Zhang, Yu Solé-Casals, Jordi Data and Signal Processing Research Group University of Vic-Central University of Catalonia Catalonia Vic08500 Spain Image Processing Research Group RIKEN Center for Advanced Photonics Riken Saitama Wako-Shi Japan Faculty of Health Data Science Juntendo University Chiba Urayasu Japan Department of Bioengineering Lehigh University BethlehemPA18015 United States Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation Nankai University Tianjin China Instituto Argentino de Radioastronomía-CCT La Plata CONICET/ CIC-PBA/ UNLP V. Elisa 1894 Argentina Department of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States Department of Psychiatry University of Cambridge CambridgeCB20SZ United Kingdom

Alzheimer’s disease is a progressive form of memory loss that worsens over time. Detecting it early, when memory issues are mild, is crucial for effective interventions. Recent advancements in computer technology, specifically Graph Convolutional Networks (GCNs), have proven to be powerful tools for analyzing Magnetic Resonance Imaging (MRI) data comprehensively. In this study, we developed a GCN framework for diagnosing mild cognitive impairment (MCI) by examining the functional connectivity (FC) derived from resting-state functional MRI (rfMRI) data. Our research systematically explored various types and processing methods of FC, evaluating their performance on the OASIS-3 dataset. The experimental results revealed several key findings. On the one hand, the proposed GCN exhibited significantly superior performance over both the baseline GCN and the Support Vector Machine (SVM) models, with statistically significant differences. It attained the highest average accuracy of 80.3% and a peak accuracy of 88.2%. On the other hand, the GCN frame work obtained using individual FCs showed overall slightly better performance than the one using global FCs. However, it is important to note that GCNs using global networks with appropriate connectivity can achieve comparable or even better performance than individual networks in certain cases. Finally, our results also indicate that the connectivity within specific brain regions, such as VIS, DMN, SMN, VAN, and FPC, may play a more significant role in GCN-based MRI classification for MCI diagnosis. These findings significantly contribute to the understanding of neurodegenerative disorders and offer valuable insights into the diverse ap plications of GCNs in brain analysis and disease detection. © 2024 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Neurodegenerative diseases

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Eye movement patterns are similar during accurate multiple-target tracking

Eye movement patterns are similar during accurate multiple-t...

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International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Kamyar Bagha Shiva Kamkar Hamid Abrishami Moghaddam Lauri Oksama Jie Li Jukka Hyönä Computer Engineering Department Khatam University Tehran Iran Machine Vision and Medical Image Processing (MVMIP) Laboratory Faculty of Electrical Engineering K.N.Toosi University of Technology Tehran Iran Center for International Scientific Studies and Collaboration (CISSC) Tehran Iran Department of Psychology and Speech-Language Pathology University of Turku Turku Finland Center for Cognition and Brain Disorders Hangzhou Normal University Hangzhou China

ISBN: (数字)9798350378245

ISBN: (纸本)9798350378252

Understanding how the brain works is a base of cognitive info-communication. To this aim we focus on multiple target tracking (MTT) as a key task that involves two important cognitive factors, attention and memory. Humans track multiple objects in their daily life while facing various challenges including occlusion and set-size. Eye movement research has shown that there are within and between subjects’ differences in scanpaths while performing MTT tasks. However, it is unclear if there is a winning scan pattern that would lead to a successful tracking of targets. To answer this question, we used dynamic time warping to compare the similarities between subjects’ scan patterns during an MTT task with different challenges. We studied the effect of set-size, occlusion, and trial response on the similarities. Then a mixed effect analysis was applied on the output to measure whether the findings were statistically significant. Results demonstrated that scan patterns were more similar when MTT task was performed correctly. It suggests that there is a common tracking strategy adopted by the viewers that leads to a correct response. Decoding this strategy has countless applications in the fields including human-computer interaction, brain-modeling and cognitive info-communication.

关键词： Human computer interaction Visualization Target tracking Accuracy Predictive models Communications technology Time measurement Cognition Decoding Cognitive science

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Visual-Semantics Embedding for Deep Hashing-Based Multi-Label Video Retrieval

SSRN

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SSRN 2023年

作者： Yue, Yuanhao Zou, Qin Cao, Ling Yu, Hongkai Chen, Chi Li, Na School of Cyber Science and Engineering Wuhan University Wuhan430072 China School of Computer Science Wuhan University Wuhan430072 China Department of Electrical Engineering and Computer Science Cleveland State University Cleveland37312 United States State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China TheArchives of Wuhan University Wuhan University Wuhan430072 China

With the explosive growth of short videos in mobile multimedia systems, there is an urgent need for efficient video retrieval. In the past decade, deep hashing technology has occupied a dominant position in content-based visual retrieval and has achieved remarkable success in image retrieval. However, for video retrieval, existing hashing-based methods still have a lot of room for improvement, especially concerning multi-label videos. There are mainly two reasons: 1) The similarity is not well-defined for multi-label videos. Traditionally, pairwise similarity is defined purely based on the explicit text information of the labels, which ignores the implicit semantic relations between them, leading to inaccurate distance measurements;2) Time-series information of video frames is not properly used in feature extraction. Most methods assign equal weight to each frame in their models, neglecting the fact that the content of a video is often determined by several keyframes. To solve the above problems, a novel video hashing method is proposed in this paper. First, a visual-semantics embedding soft similarity is developed to calculate the distance between pairwise videos, where the implicit semantic association of labels is learned by a graph convolution network (GCN). Second, a hybrid attention module, consisting of a self-attention block and a relation-attention block, is integrated into the hashing network. The attention module assigns different weights to the frames according to their importance in determining the content of the video. Experimental results show that the proposed method achieves significant improvements over competing ones. © 2023, The Authors. All rights reserved.

关键词： Convolution

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A review of characteristics influencing factors, mechanism, characteristics of heavy metal pollution in farmland in China 41

A review of characteristics influencing factors, mechanism, ...

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41st Asian Conference on remote sensing, ACRS 2020

作者： Li, Xuqing Li, Chen Jin, Yongtao Liu, Di Liu, Yuanping Wang, Shuai Wang, Ruoyu Chen, Hong Wu, Zhengjie Department of Computer and Remote Sensing Information Technology North China Institute of Aerospace Engineering Langfang065000 China Hebei Collaborative Innovation Center for Aerospace Remote Sensing Information Processing and Application Langfang065000 China Beijing Institute of Space Mechanics and Electricity Aeronautical Optical Remote Sensing laboratory Beijing100076 China

ISBN: (纸本)9781713829089

In this paper, the characteristics and current situation of heavy metal pollution in farmland in China, the influencing factors and mechanism of heavy metal pollution in crops, the summary of remote sensing monitoring methods for heavy metal pollution in farmland and the research prospect are discussed. Farmland non-point source pollution monitoring is an important issue related to national security and stability and people's livelihood. At present, the characteristics of heavy metal pollution in farmland in China mainly include:①Heavy metal pollution is prevalent in *** three major rice dominant areas in China (the rice dominant area in the northeast plain, the rice dominant area in the Yangtze river basin, and the rice dominant area in the southeast coast) are all polluted by heavy metals to varying degrees. ②Heavy metal pollution is serious in farmland. The pollutants are mainly As, Cd, Pb, Hg, Ni, Cu, DDT and pahs.③Heavy metal contamination of grain occurs frequently. Heavy metal polluted farmland may cause crop diseases. In this paper, the spatial distribution characteristics of As, Cu, Cd, Cr, Pb and Zn in China are summarized. The content of heavy metal elements in crops largely depends on the characteristics and types of crops as well as the growing environment. © 2020 ACRS 2020 - 41st Asian Conference on remote sensing. All rights reserved.

关键词： Crops

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Minimal-time Deadbeat Consensus and Individual Disagreement Degree Prediction for High-order Linear Multi-agent Systems

arXiv

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arXiv 2023年

作者： Hu, Fu-Long Zhang, Hai-Tao Xu, Bowen Hu, Zhe Ren, Wei School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University Xi’an710072 China Department of Electrical and Computer Engineering University of California Riverside92521 United States

In this paper, a Hankel matrix-based fully distributed algorithm is proposed to address a minimal-time deadbeat consensus prediction problem for discrete-time high-order multi-agent systems (MASs). Therein, each agent can predict the consensus value with the minimum number of observable historical outputs of its own. Accordingly, compared to most existing algorithms only yielding asymptotic convergence, the present method can attain deadbeat consensus instead. Moreover, based on the consensus value prediction, instant individual disagreement degree value of MASs can be calculated in advance as well. Sufficient conditions are derived to guarantee both the minimal-time deadbeat consensus and the instant individual disagreement degree prediction. Finally, both the effectiveness and superiority of the proposed deadbeat consensus algorithm are substantiated by numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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Multi-modal signal integration for enhanced sleep stage classification: Leveraging EOG and 2-channel EEG data with advanced feature extraction

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Artificial Intelligence in Medicine 2025年 166卷 103152-103152页

作者： Samaee, Mahdi Yazdi, Mehran Massicotte, Daniel Signal and Image Processing Laboratory School of Electrical and Computer Engineering Shiraz University Shiraz Iran Laboratory of Signal and System Integration Department of Electrical and Computer Engineering Université du Québec à Trois-Rivières Trois-Rivières Canada

This paper introduces an innovative approach to sleep stage classification, leveraging a multi-modal signal integration framework encompassing Electrooculography (EOG) and two-channel electroencephalography (EEG) data. We explore the utility of various feature extraction techniques, including Short-Time Fourier Transform (STFT), Wavelet Transform, and raw signal processing, alongside the utilization of neural networks as feature extractors. This unique combination allows us to harness the benefits of traditional feature extraction methods while capitalizing on the power of neural networks to enhance classification performance. Our comprehensive classifier evaluation encompasses a range of models, including Long Short-Term Memory (LSTM) networks and XGBoost. Remarkably, our results reveal exceptional performance with the XGBoost classifier, achieving an overall accuracy of 84.57 % and a macro-F1 score of 78.21 % on the Sleep-EDF expanded dataset, and an overall accuracy of 86.02 % and a macro-F1 score of 81.96 % on the ISRUC-Sleep dataset. Class-specific accuracies highlight its proficiency, particularly in detecting wake and N2 stages, solidifying its superiority among the classifiers tested. This amalgamation of feature sets, complemented by Principal Component Analysis (PCA) for dimensionality reduction, underscores its significance in yielding top-tier classification outcomes. The integration of traditional feature extraction methods with neural networks as feature extractors creates a robust and comprehensive system for sleep stage classification, offering the advantages of both approaches to enhance the accuracy and reliability of the results. © 2025

关键词： Discrete wavelet transforms

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Uncertainty-aware Sampling for Long-tailed Semi-supervised Learning

arXiv

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arXiv 2024年

作者： Yang, Kuo Li, Duo Hu, Menghan Zhai, Guangtao Yang, Xiaokang Zhang, Xiao-Ping The Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China The Kargobot of DiDi Shanghai201210 China The Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Tsinghua Berkeley Shenzhen Institute Shenzhen China The Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University ONM5B 2K3 Canada

For semi-supervised learning with imbalance classes, the long-tailed distribution of data will increase the model prediction bias toward dominant classes, undermining performance on less frequent classes. Existing methods also face challenges in ensuring the selection of sufficiently reliable pseudo-labels for model training and there is a lack of mechanisms to adjust the selection of more reliable pseudo-labels based on different training stages. To mitigate this issue, we introduce uncertainty into the modeling process for pseudo-label sampling, taking into account that the model performance on the tailed classes varies over different training stages. For example, at the early stage of model training, the limited predictive accuracy of model results in a higher rate of uncertain pseudo-labels. To counter this, we propose an Uncertainty-Aware Dynamic Threshold Selection (UDTS) approach. This approach allows the model to perceive the uncertainty of pseudo-labels at different training stages, thereby adaptively adjusting the selection thresholds for different classes. Compared to other methods such as the baseline method FixMatch, UDTS achieves an increase in accuracy of at least approximately 5.26%, 1.75%, 9.96%, and 1.28% on the natural scene image datasets CIFAR10-LT, CIFAR100-LT, STL-10-LT, and the medical image dataset TissueMNIST, respectively. The source code of UDTS is publicly available at: https://***/yangk/UDTS. Copyright © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Few-Shot Class-Incremental Learning with Prior Knowledge

arXiv

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arXiv 2024年

作者： Jiang, Wenhao Li, Duo Hu, Menghan Zhai, Guangtao Yang, Xiaokang Zhang, Xiao-Ping Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Kargobot of DiDi Shanghai201210 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Tsinghua Berkeley Shenzhen Institute Shenzhen China Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University ONM5B 2K3 Canada

To tackle the issues of catastrophic forgetting and overfitting in few-shot class-incremental learning (FSCIL), previous work has primarily concentrated on preserving the memory of old knowledge during the incremental phase. The role of pre-trained model in shaping the effectiveness of incremental learning is frequently underestimated in these studies. Therefore, to enhance the generalization ability of the pre-trained model, we propose Learning with Prior Knowledge (LwPK) by introducing nearly free prior knowledge from a few unlabeled data of subsequent incremental classes. We cluster unlabeled incremental class samples to produce pseudo-labels, then jointly train these with labeled base class samples, effectively allocating embedding space for both old and new class data. Experimental results indicate that LwPK effectively enhances the model resilience against catastrophic forgetting, with theoretical analysis based on empirical risk minimization and class distance measurement corroborating its operational principles. The source code of LwPK is publicly available at: https://***/StevenJ308/LwPK. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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