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检索条件"任意字段=International Congress on Image and Signal Processing, BioMedical Engineering and Informatics"

共 2036 条记录，以下是211-220 订阅

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An Adaptive Fuzzy Closed-loop Control of Neural Activity Based on Neural Mass Model 16

An Adaptive Fuzzy Closed-loop Control of Neural Activity Bas...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Ma, Lerong Li, Shanshan Tang, Xiaomin Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing & Intelligent Control School of Automation and Electrical Engineering Tianjin China

ISBN: (纸本)9798350330755

An adaptive fuzzy closed-loop control method using linear feedback technology was proposed to inhibit epileptiform activity simulated by a neural mass model. In this study, the problem of epileptic suppression was considered as a typical control problem. First, the neural mass model was introduced to simulate neural firings at all stages of epileptic seizure;secondly, an adaptive fuzzy closed-loop control strategy was designed to suppress epileptiform firings of neural mass model;finally, the control effect of this adaptive fuzzy closed-loop control was compared with incremental PI closed-loop control. The results showed that the adaptive fuzzy closed-loop control consumes less control energy and achieves better control effect comparing with the incremental PI control algorithm. In addition, the adaptive closed-loop control can adjust the parameters in real time according to the epilepsy state and does not depend on the change of model parameters, which providing a theoretical basis for individualized medical treatment. © 2023 IEEE.

关键词： Closed loop control systems

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Deep Learning-Based Intelligent Aided Diagnosis System for Ophthalmic Ultrasound images 17

Deep Learning-Based Intelligent Aided Diagnosis System for O...

引用

17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Li, Zemeng Zhou, Sheng Institute of Biomedical Engineering Chinese Academy of Medical Sciences Peking Union Medical College Tianjin China Tianjin Institutes of Health Science Chinese Academy of Medical Science Peking Union Medical College State Key Laboratory of Advanced Medical Materials and Devices Tianjin China

ISBN: (纸本)9798331507398

Objective: To design an intelligent assisted diagnosis system for ophthalmic ultrasound images based on a deep learning approach, which serves as an intelligent clinical diagnostic aid analysis tool for posterior ocular segment diseases. Methods: In order to accomplish multi-level feature extraction and fusion, two pre-trained network models-InceptionV3-Xception - are concatenated to create the InceptionV3-Xception fusion model. A classifier better suited for the task of multiple classification and recognition of ophthalmic ultrasound images is created in order to categorize 3402 ophthalmic ultrasound images. The model evaluation metrics include accuracy, macro-average precision, macro-average sensitivity, macro-average F1 value, subject working feature curves, and area under the curve. The model's credibility is evaluated by testing the model's decision basis using a gradient-weighted class activation mapping method. Complete developing the ophthalmic ultrasound image intelligent assisted diagnosis system's human-computer interface using the PyQt toolkit. Results: The Inception V3-Xception fusion model' s accuracy, precision, sensitivity, and area under the subject working feature curve on the test set were 0.9673, 0.9521, 0.9528, and 0.9988, respectively. The model's decision basis aligned with ophthalmologists' clinical diagnosis rationale. The auxiliary diagnosis system is portable, reliable, and has an easy-to-use interface. It can recognize ocular ultrasound images intelligently and analyze thermograms in an interpretable manner. Conclusion: The deep learning-based intelligent aided diagnosis system for ocular ultrasound images, which can quickly and accurately identify six distinct types of ophthalmic ultrasound images, aids in the intelligent evolution of ophthalmic clinical diagnosis. © 2024 IEEE.

关键词： Ultrasonic imaging

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Efficient Transfer Learning Methods Using Parameterization in Few-Shot Speech Recognition 16

Efficient Transfer Learning Methods Using Parameterization i...

引用

16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Jin, Hui Jin, Xiaofeng Yu, Mingdong Wang, Guirong Yanbian University College of Engineering Yanji133002 China Yanbian University Inst. of Intelligent Information Processing Yanji China

ISBN: (纸本)9798350330755

Fine-tuning self-supervised models is an effective transfer learning method for speech tasks, exhibiting exceptional performance in low-resource speech recognition scenarios. However, self-supervised pre-trained models contain a large number of parameters, resulting in high fine-tuning costs. When adapters are employed during the fine-tuning process of pre-trained models for specific tasks, they substantially reduce the number of adjustable parameters while minimizing performance degradation. The recent introduction of adapter methods in the field of speech recognition research has yielded significant achievements. To further reduce the number of fine-tuning parameters and improve the adaptability of adapters to specific tasks, this paper first introduces the Houlsby adapter into the Wav2Vec2.0 model and then adopts the LN-Tuning method for joint participation in downstream task fine-tuning, resulting in only 2.4% of the model's parameters being involved in the fine-tuning process. Experimental results demonstrate that the methods employed in this paper outperform previous adapter methods in terms of performance, with only a marginal difference of 0.4% compared to fine-tuning alone. Furthermore, these techniques facilitate more efficient model training and have yielded promising outcomes on other self-supervised models. © 2023 IEEE.

关键词： Speech recognition

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Multi-domain Integrative Structure-enhanced and Sparse-coding Network for Sparse View CT Reconstruction 16

Multi-domain Integrative Structure-enhanced and Sparse-codin...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Liu, Jin Sun, Yu Kang, Yanqin Liu, Guiru Li, Zheng Anhui Polytechnic University College of Computer and Information Wuhu China Technology CO. Ltd Wuhu China

ISBN: (纸本)9798350330755

In order to decrease X-ray radiation dose and imaging time, it is crucial to employ a significant technique of minimizing the number of projections. Nevertheless, the presence of down sampling projection data may result in a decline in CT image quality caused by prominent streak artifacts. In this study, we present a novel network called MISS-net (multi-domain integrative structure-enhanced and sparse-coding network) to enhance image quality using sparse-view data. To begin with, MISS-net integrates hybrid domain features from sinogram data and CT images through a two-step network structure. Furthermore, in each stage, a dedicated subnetwork for enhancing tiny tissue is utilized to ensure the preservation of texture, while a subnetwork for extracting features through learned convolutional sparse coding is employed. Next, a distinct domain transformation converts the projection into an image, facilitating the exchange of information between the two domains. Experimental results shown that our proposed MISS-net outperformed the comparison methods in terms of image quality, exhibiting enhanced small features and sharp tissue. Additionally, it exhibited minimal structural loss in image quality, as evidenced by the experimental results on 64 views MAYO clinical datasets. © 2023 IEEE.

关键词： image quality

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Multi-Scale Hybrid Embedding Transformer for Atrophic Gastritis Recognition 17

Multi-Scale Hybrid Embedding Transformer for Atrophic Gastri...

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17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Jiang, Shanzhi Cao, Yankun Mukhopadhyay, Subhas Chandra Min, Xiang Li, Wentao Lv, Chaoyang Li, Zhen Yang, Xiaoyun Liu, Zhi School of Information Science and Engineering Shandong University Qingdao266237 China School of Software Shandong University Jinan250101 China School of Engineering Macquarie University SydneyNSW2109 Australia Qilu Hospital of Shandong University Department of Ultrasound Shandong Jinan250012 China Linyi County People's Hospital Department of Gastroenterology Dezhou251599 China Qilu Hospital of Shandong University Department of Gastroenterology Shandong Jinan250012 China

ISBN: (纸本)9798331507398

Atrophic gastritis is a chronic gastric disease that can be identified through gastroscope observation. Automatically identifying atrophic gastritis and its location through endoscopic images can effectively reduce the burden on doctors. However, the similarity of adjacent areas and the less obvious nature of lesions pose significant challenges to existing diagnostic methods. In this paper, we propose a novel method called Multi-scale Hybrid Embedding Transformer (MHET). MHET can capture multi-scale features from images to address this issue, achieving more accurate recognition of atrophic gastritis. Furthermore, we utilize generative adversarial networks (GANs) to synthesize endoscopic images, thereby resolving the problem of data imbalance. We have collected an atrophic gastritis dataset and conducted model training, as well as related experiments. The results indicate that our method achieves high performance on multiple evaluation metrics and its effectiveness is validated through ablation studies. © 2024 IEEE.

关键词： Endoscopy

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A Fingerprint Collection and Enhancement Method for Indoor Localization 16

A Fingerprint Collection and Enhancement Method for Indoor L...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Xue, Yuanbo Zhao, Kun Zheng, Zhengqi Yu, Chao Xing, Jingyi Dai, Wenchi Yang, Kexin East China Normal University Engineering Center of Shmec for Space Information and Gnss Shanghai200241 China East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing Shanghai200241 China

ISBN: (纸本)9798350330755

With the rapid development of modern science and technology, as well as the large-scale deployment and application of networks, Location Based Service (LBS), as an emerging industry, has played an increasingly important role in people's daily life and work. Fingerprint localization technology is an indoor localization method based on wireless signal fingerprint, widely used in indoor environments such as shopping malls, hospitals, airports, etc. However, in practical deployment, fingerprint collection work requires a large amount of time and human resources, which seriously restricts the development of fingerprint positioning technology. To address this issue, this article introduces a method for fingerprint collection and data augmentation, which can effectively reduce the time and workload of fingerprint collectors during fingerprint collection, thereby shortening the entire deployment process of fingerprint localization technology. This method grids the fingerprint collection area and collects a small amount of data within each grid. Through data augmentation, a large amount of effective fingerprint data is quickly obtained, thereby improving the accuracy and accuracy of fingerprint localization technology. The experimental results show that compared to the original data, the positioning accuracy of this method only decreases by a few centimeters when using 30% of the original data. Therefore, this method has certain application prospects and can play a positive role in the current LBS field, providing people with more accurate and convenient positioning services. © 2023 IEEE.

关键词： Location based services

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image Visibility Graph Analysis of the Eye Disease images 16

Image Visibility Graph Analysis of the Eye Disease Images

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Wu, Dandan Ma, Zhiyi Yang, Xiaodong Jiang, Guangkang Yan, Chen Ma, Lu China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Suzhou Vocational and Technical College Suzhou234000 China

ISBN: (纸本)9798350330755

Visibility graph algorithm is a widely used time series complex network transformation algorithm. It has been mainly applied to one-dimensional or multidimensional time series, while many real-life objects, such as images, cannot be represented by sequences. In this paper, we focus on two-dimensional images and propose a novel method of image visibility graph network (IVGN) with redefined node-degree features. We use the staining images of corneal ulcer of eye as the research object and demonstrate the application of IVGN to medical images. Our method consists of the following steps: (1) We construct a novel IVGN by treating each pixel as a node and connecting the visible pixels. We compute the average node degree of the IVGN for ocular pathology image analysis. (2) Based on the IVGN, we extract the node-degree features of the visible images and map them to a new degree topology, which is used for image enhancement. (3) By doing so, we build a gray co-occurrence matrix based on the degree topology and extract its four parameters, showing the role of the degree topology in image texture extraction. (4) As a result, we use the proposed contrast-based descriptors to achieve effective detection of eye staining images with different categories and severity levels. © 2023 IEEE.

关键词： Pixels

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Big Data Retrieval and Its Application in image Annotation and Reconstruction 15

Big Data Retrieval and Its Application in Image Annotation a...

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15th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2022

作者： Liu, Hui Liaoning Institute Science and Technology School of Sugon Big Data Benxi China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

This paper discusses how to organically combine image retrieval with data mining, image reconstruction and other related technologies. It makes use of the prior knowledge and rich images contained in the big data, and the automatic annotation and reconstruction-sharing application in the images are driven by text messages, so as to realize the open real-time annotation of the large-scale images and the real-time sharing of the high-resolution photos in the mobile phones. In this paper, the big data retrieval and its application in the image annotation and reconstruction are analyzed. © 2022 IEEE.

关键词： image reconstruction

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Attention-based convolution-recurrent neural network for cross-subject EEG emotion recognition 16

Attention-based convolution-recurrent neural network for cro...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Han, Zhichao Su, Jipu Qin, Teng Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing210096 China Southeast University School of Information Science and Engineering Nanjing210096 China Shanghai Second Light Industry School Shanghai200135 China

ISBN: (纸本)9798350330755

In contemporary applications of emotional brain-computer interfaces, the experimental data frequently originate from distinct individuals, leading to considerable inter-individual variance. This variance poses challenges in generalizing models to novel, unseen individuals. This study introduces a novel approach to cross-subject EEG emotion recognition, leveraging a spatial-temporal neural network integrated with an attention mechanism. This approach sequentially extracts spatial and temporal domain features, effectively mitigating inter-subject variability by discerning emotionally salient features through the attention module. This refinement significantly enhances classification performance. The network operates end-to-end, initially eliminating the baseline signal from the input raw EEG data and subsequently segmenting it into windows for preprocessing. Specifically, the spatial attributes of diverse subjects' EEG data are discerned through a spatial attention-based CNN network, while temporal dynamics are captured via a self-attention-based LSTM network. Finally, the two sets of features are amalgamated for cross-subject EEG emotion recognition. Empirical findings demonstrate that this methodology can derive more distinctive features from the original EEG signals, achieving an average classification accuracy of 89.29% on the DEAP dataset. This constitutes a noteworthy enhancement in classification efficacy compared to alternative methods. These results offer methodological insights pertinent to emotional brain-computer interface systems in authentic scenarios. © 2023 IEEE.

关键词： Emotion Recognition

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Acceleration of Multi-Direction Diffusion MRI Using EPI with Keyhole (EPIK) and Locally Low-Rank Tensor Reconstruction 16

Acceleration of Multi-Direction Diffusion MRI Using EPI with...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Aburas, Ahmed Tang, Xin Gao, Juan Hu, Chenxi Shanghai Jiao Tong University The Institute of Medical Imaging Technology School of Biomedical Engineering Shanghai China

ISBN: (纸本)9798350330755

Diffusion MRI is a commonly used non-invasive imaging technique with wide range of clinical applications. Many of these applications benefit from high spatial and angular resolution acquisitions as they increase the accuracy and precision of the generated quantitative maps. Higher resolution is attainable by using multi-shot EPI, which increases the scan time. To maintain the same scan efficiency as single-shot EPI, we propose an end-to-end framework that comprises an accelerated EPI-with-keyhole (EPIK) acquisition and a novel reconstruction algorithm based on space-coil-contrast locally low-rank tensor (SCC-LLRT) regularization. We compared the proposed SCC-LLRT algorithm with SPA-LLR for reconstructing EPIK data, and compared the proposed framework with a more traditional acceleration framework based on a combination of regular EPI and SPA-LLR, using retrospectively undersampled data from a healthy subject. The results showed that SCC-LLRT generated more accurate reconstruction with better preservation of high-frequency structural details compared with SPA-LLR. Furthermore, the proposed framework (EPIK+SCC-LLRT) achieved a substantially improved NRMSE, SSIM and reduction of ghosting artifacts compared with the traditional acceleration framework (EPI+SCC-LLRT). The results suggest that the proposed framework has a high feasibility for accelerating multi-direction diffusion MRI. © 2023 IEEE.

关键词： Tensors

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