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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Luo, Yijing Han, Bo Gong, Chen PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Jiangsu Key Lab of Image Video Understanding for Social Security China Department of Computer Science Hong Kong Baptist University Hong Kong Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9780999241165

Practically, we often face the dilemma that some of the examples for training a classifier are incorrectly labeled due to various subjective and objective factors. Although intensive efforts have been put to design classifiers that are robust to label noise, most of the previous methods have not fully utilized data distribution information. To address this issue, this paper introduces a bi-level learning paradigm termed "Spectral Cluster Discovery" (SCD) for combating with noisy labels. Namely, we simultaneously learn a robust classifier (Learning stage) by discovering the low-rank approximation to the ground-truth label matrix and learn an ideal affinity graph (Clustering stage). Specifically, we use the learned classifier to assign the examples with similar label to a mutual cluster. Based on the cluster membership, we use the learned affinity graph to explore the noisy examples based on the cluster membership. Both stages will reinforce each other iteratively. Experimental results on typical benchmark and real-world datasets verify the superiority of SCD to other label noise learning methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Iterative methods

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Highly Stretchable, Sensitive and Healable Polyurethane-Urea/Graphene Nanocomposite Sensor for Multifunctional Applications

SSRN

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SSRN 2023年

作者： Ma, Jun Yu, Yin Xu, Zhaoyang Xu, Lisheng Li, Yikuan Liu, Tianqing Meng, Qingshi Su, Xiao Kuan, Hsu-Chiang Dai, Jiabin College of Medicine and Bioinformatics Engineering Northeastern University Shenyang110819 China College of Aerospace Engineering Shenyang Aerospace University Shenyang110136 China Neusoft Research of Intelligent Healthcare Technology Co. Ltd Shenyang110169 China Key Laboratory of Medical Image Computing Ministry of Education Shenyang110169 China NICM Health Research Institute Western Sydney University Westmead2145 Australia Shenyang Aircraft Design Institute Shenyang110035 China School of Engineering Future Industries Institute University of South Australia Mawson LakesSA5095 Australia Department of Chemical and Materials Engineering Southern Taiwan University of Science and Technology Tainan City710301 Taiwan

Flexible wearable electronic devices offer promising potential for monitoring physiological signals. However, creating a single sensor that integrates high tensile strength, sensitivity, self-healing capabilities, and a wide working range presents a significant and multifaceted challenge. This study reports a novel nanocomposite consisting of polyurethane-urea elastomer (PUU) and graphene nanoplatelets (E-GNPs) that are mechanochemically modified with diethyltoluene diamine ethacure 100. A low electrical percolation threshold was observed at 4.17 vol% E-GNPs. The nanocomposite-based sensor has high stretchability (361.76%), strength (19.73 MPa), and sensitivity (gauge factor up to 17.57). It can withstand 20,000 cycles at 50% strain. The sensor exhibits negative temperature dependence at 20–100 °C, with a resolution of 0.01/°C at 36–40 °C. Treatments with solvents and heat enable a healing efficiency for sensitivity of up to 70.46%. The healable sensor enables real-time monitoring of temperature and strain signals, making it ideal for wearable devices in human health and sports monitoring. © 2023, The Authors. All rights reserved.

关键词： Graphene

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Progressive Graph Convolution Network for EEG Emotion Recognition

arXiv

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arXiv 2021年

作者： Zhou, Yijin Li, Fu Li, Yang Ji, Youshuo Shi, Guangming Zheng, Wenming Zhang, Lijian Chen, Yuanfang Cheng, Rui The Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education The School of Artificial Intelligence Xidian University Xi'An710071 China The Key Laboratory of Child Development and Learning Science Ministry of Education School of Biological Sciences and Medical Engineering Southeast University Jiangsu Nanjing210096 China The Beijing Institute of Mechanical Equipment Beijing100000 China

Studies in the area of neuroscience have revealed the relationship between emotional patterns and brain functional regions, demonstrating that dynamic relationships between different brain regions are an essential factor affecting emotion recognition determined through electroencephalography (EEG). Moreover, in EEG emotion recognition, we can observe that clearer boundaries exist between coarse-grained emotions than those between fine-grained emotions, based on the same EEG data;this indicates the concurrence of large coarse- and small fine-grained emotion variations. Thus, the progressive classification process from coarse- to fine-grained categories may be helpful for EEG emotion recognition. Consequently, in this study, we propose a progressive graph convolution network (PGCN) for capturing this inherent characteristic in EEG emotional signals and progressively learning the discriminative EEG features. To fit different EEG patterns, we constructed a dual-graph module to characterize the intrinsic relationship between different EEG channels, containing the dynamic functional connections and static spatial proximity information of brain regions from neuroscience research. Moreover, motivated by the observation of the relationship between coarse- and fine-grained emotions, we adopt a dual-head module that enables the PGCN to progressively learn more discriminative EEG features, from coarse-grained (easy) to fine-grained categories (difficult), referring to the hierarchical characteristic of emotion. To verify the performance of our model, extensive experiments were conducted on two public datasets: SEED-IV and multi-modal physiological emotion database (MPED). The experiment results show that the PGCN achieves a state-of-art performance. Furthermore, we explored the effect of different frequency bands based on our model, and visualized the activated brain regions. The experiment results reveal the relationship between human emotion and high-frequency EEG signals, as

关键词： Convolution

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Exploring Separable Attention for Multi-Contrast MR image Super-Resolution

arXiv

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arXiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Yu, Kai Xu, Yong Yang, Jian Shao, Ling Fu, Huazhu The Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China The Institute of High Performance Computing A*STAR Singapore138632 Singapore The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China The Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Terminus Group China

Super-resolving the Magnetic Resonance (MR) image of a target contrast under the guidance of the corresponding auxiliary contrast, which provides additional anatomical information, is a new and effective solution for fast MR imaging. However, current multi-contrast super-resolution (SR) methods tend to concatenate different contrasts directly, ignoring their relationships in different clues, e.g., in the high-intensity and low-intensity regions. In this study, we propose a separable attention network (comprising high-intensity priority attention and low-intensity separation attention), named SANet. Our SANet could explore the areas of high-intensity and low-intensity regions in the "forward" and "reverse" directions with the help of the auxiliary contrast, while learning clearer anatomical structure and edge information for the SR of a target-contrast MR image. SANet provides three appealing benefits: (1) It is the first model to explore a separable attention mechanism that uses the auxiliary contrast to predict the high-intensity and low-intensity regions regions, diverting more attention to refining any uncertain details between these regions and correcting the fine areas in the reconstructed results. (2) A multi-stage integration module is proposed to learn the response of multi-contrast fusion at multiple stages, get the dependency between the fused representations, and boost their representation ability. (3) Extensive experiments with various state-of-the-art multi-contrast SR methods on fastMRI and clinical in vivo datasets demonstrate the superiority of our model. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Extension of Sample Dimension and Sparse Representation Based Classification of Low-Dimension Data 12th

Extension of Sample Dimension and Sparse Representation Base...

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12th International Conference on Genetic and Evolutionary computing, ICGEC 2018

作者： Wang, Qian Zhang, Yongjun Xiao, Ling Li, Yuewei Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (纸本)9789811358401

As we know, sparse representation methods can achieve high accuracy for classification of high-dimensional data. However, they usually show poor performance in performing classification of low-dimensional data. In this paper, the increase of the sample dimension for sparse representation is studied and surprising accuracy improvement is obtained. The paper has the following main value. First, the designed method obtains promising results for classification of low-dimensional data and is very useful for widening the applicability of sparse representation. The accuracy of the designed method may be 10% higher than that of sparse representation based on original samples. To our knowledge, no similar work is available. Second, the designed method is simple and has a low computational cost. Extensive experiments are conducted and the experimental results also show that the designed method can be applied to improve other methods too. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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A nanoprobe-integrated capillary sensing system for highly sensitive SERS detection of neutral α-glucosidase in semen

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International Journal of Biological Macromolecules 2025年 317卷 144847页

作者： Hongguo Wei Fan Xu Tao Xu Yunxin Wang Deliang Li Yuetian Ren Zejun Yan Zhe Zhang Keren Chen Shuo Chen College of Medicine and Biological Information Engineering Northeastern University Shenyang 110169 China Department of Urology The First Affiliated Hospital of Ningbo University Ningbo 315010 China Department of Urology The First Affiliated Hospital of China Medical University Shenyang 110001 China Foshan Graduate School of Innovation Northeastern University Foshan 528311 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang 110169 China

The incidence of male infertility is increasing year by year, with obstructive azoospermia being the main cause of infertility. The activity of neutral α-glucosidase plays an important role in diagnosing obstructive azoospermia. In order to effectively and non-invasively distinguish obstructive azoospermia, we have developed a rapid and sensitive surface-enhanced Raman spectroscopy (SERS) detection platform for neutral α-glucosidase in semen by combining nanoprobes with capillary systems labeled with capture antibodies. This platform utilizes a minimally invasive sampling method of capillary, avoiding the complex sampling and separation steps in conventional clinical examinations. By combining Ag@MnO 2 core-shell composite nanoparticles prepared through rational design with tetramethylbenzidine (TMB), precise differentiation of obstructive azoospermia patients and healthy donors can be achieved in a short time. The successful implementation of nanoprobe-integrated capillary sensing system provides a novel, efficient, and clinically feasible method for highly sensitive detection of neutral α-glucosidase in semen and precise diagnosis of obstructive azoospermia, with broad prospects for biomedical applications.

关键词： Ag@MnO(2) Capillary system Neutral α-glucosidase detection SERS

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A noise resistant method by setting thresholds for use with face recognition 3

A noise resistant method by setting thresholds for use with ...

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3rd International Conference on Advances in image Processing, ICAIP 2019

作者： Li, Yuewei Zhang, Yongjun Wang, Qian Xiao, Ling Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (纸本)9781450376754

Face recognition is one of the important research contents of biometric recognition, and it is widely used in various fields of life. The recognized face image is acquired by devices such as video capture, and noise is generated by device factors or external influences during the acquisition process, thereby reducing the accuracy of recognition. If the noise intensity is basically fixed, the smaller the pixel value is, the larger the proportion of noise is. This pixel has low reliability for image description. Therefore, a method of noise resistant is proposed from the angle of image preprocessing to reduce the impact of this problem. This anti-noise method first sets a certain threshold. If the pixel value in the image is lower than it, the pixel values below the set value will be directly set to 0. Then, using the classification algorithm of cosine similarity, the similarity calculation is performed on the face image, so that the image is classified, finally recognized, and the results are output. The experimental results show that after the appropriate threshold is selected, the proposed method can reduce the interference of noise on the classification and improve the accuracy of face recognition. © 2019 Association for computing Machinery.

关键词： Face recognition

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RCGA-Net: An Improved Multi-hybrid Attention Mechanism Network in Biomedical image Segmentation

RCGA-Net: An Improved Multi-hybrid Attention Mechanism Netwo...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Xiao, Feng Shen, Cong Chen, Yu Yang, Tian Chen, Shengyong Liao, Zhijun Tang, Jijun Tianjin University of Technology School of Computer Science and Engineering Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China School of Basic Medical Sciences Fujian Medical University Fujian Fuzhou China College of Intelligence and Computing Tianjin University Tianjin China Key Laboratory of Systems Bioengineering Ministry of Education Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department of Computer Science and Engineering University of South Carolina ColumbiaSC United States

ISBN: (纸本)9781665401265

Drawing support from an effective medical image Segmentation (MIS) is conducive to a substantial diagnostic basis for the physicians to identify the focus lesion in the patient body and give the subsequent clinical assessment of the patient status. Although various works have tried the challenging quantitative analysis problem, it is still difficult to conduct precise automatic segmentation, especially the soft tissue organs. In this decade, with the increased amount of available datasets, deep learning-based networks have achieved remarkable performance in image processing. Inspired by the state-of-the-art deep learning works, in this paper, we propose an end-to-end multi-layer network named RCGA-Net. It consists of an encoder-decoder backbone that integrates a coordinate attention mechanism based on space and channel and a global context extraction module to highlight more valuable information. To evaluate the performance of RCGA-Net, we apply it to different kinds of clinical and experimental MIS tasks to testify its generalization ability. Extensive experiments represent that our schema has taken the outperform or compatible results among the comparison methods group. Specifically, the numeric result of RCGA-Net on the pulmonary dataset has achieved a 99.12% optimum F1-score. © 2021 IEEE.

关键词： Diagnosis

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Graph Confidence Intercalibration Network for Intracranial Aneurysm Lesion Instance Segmentation in Dsa

SSRN

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SSRN 2024年

作者： Ye, Haili Mo, Yancheng Tang, Chen Liao, Mingqian Zhang, Xiaoqing Dai, Limeng Li, Baihua Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computer Science Loughborough University LoughboroughLE11 3TU United Kingdom School of Ophthalmology and Optometry Eye Hospital Wenzhou Medical University Wenzhou325027 China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen People’s Hospital Neurosurgery Department Shenzhen518020 China China

Intracranial aneurysm (IA) lesion segmentation is significant for its treatment and prognosis. Although exiting deep network-based instance methods have good IA lesion segmentation results based on digital subtraction angiography (DSA) images, they still face great challenges with instance confidence bias and imprecise boundary segmentation, which may negatively affect IA diagnosis. To tackle these problems, this paper proposes a novel graph confidence intercalibration network (GCINet) to automatically segment IA lesions from DSA images. To be specific, we design a graph confidence intercalibration (GCI) module to mitigate instance confidence bias by dynamically adjusting their confidence distributions. At the same time, we propose an edge space perception (ESP) module to correct ambiguous segmentation boundaries. Extensive experiments on a clinical IA-DSA and a publicly available LiTS dataset demonstrate that our GCINet outperforms state-of-the-art methods. Additionally, visual analysis and ablation studies are provided to verify the effectiveness of each module in GCINet. © 2024, The Authors. All rights reserved.

关键词： image segmentation

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Frequency-mixed Single-source Domain Generalization for medical image Segmentation

arXiv

引用

arXiv 2023年

作者： Li, Heng Li, Haojin Zhao, Wei Fu, Huazhu Su, Xiuyun Hu, Yan Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Medical Intelligence and Innovation Academy Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Hospital Shenzhen China Institute of High Performance Computing Agency for Science Technology and Research Singapore Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

The annotation scarcity of medical image segmentation poses challenges in collecting sufficient training data for deep learning models. Specifically, models trained on limited data may not generalize well to other unseen data domains, resulting in a domain shift issue. Consequently, domain generalization (DG) is developed to boost the performance of segmentation models on unseen domains. However, the DG setup requires multiple source domains, which impedes the efficient deployment of segmentation algorithms in clinical scenarios. To address this challenge and improve the segmentation model's generalizability, we propose a novel approach called the Frequency-mixed Single-source Domain Generalization method (FreeSDG). By analyzing the frequency's effect on domain discrepancy, FreeSDG leverages a mixed frequency spectrum to augment the single-source domain. Additionally, self-supervision is constructed in the domain augmentation to learn robust context-aware representations for the segmentation task. Experimental results on five datasets of three modalities demonstrate the effectiveness of the proposed algorithm. FreeSDG outperforms state-of-the-art methods and significantly improves the segmentation model's generalizability. Therefore, FreeSDG provides a promising solution for enhancing the generalization of medical image segmentation models, especially when annotated data is scarce. The code is available at https://***/liamheng/Non-IID medical image Segmentation. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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