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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141-150 订阅

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Bathymetry Retrieval from Hyperspectral image Using the Channel-wise Spectral Attention Based Convolutional Neural Network 16

Bathymetry Retrieval from Hyperspectral Image Using the Chan...

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

作者： Peng, Deyan Mao, Haihua Sun, Li Li, Qingli Zhou, Mei East China Normal University Academy of International Transport and Logistics Shanghai China East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing Shanghai China

ISBN: (纸本)9798350330755

Different from traditional measuring method based on the shipborne special equipment, satellite-based method has various potential advantages. This paper investigates the bathymetry retrieval problem from the hyperspectral remote sensing image. The idea is to make use of each spectral data, and fifind the relevant ones for water depth through the spectral attention weights. To fully exploit the spatial-spectral data, a convolutional neural network (CNN) is employed. It is fed with the small square hyperspectral patches and is required to output the bathymetry for the center point in the patch. The CNN has a side branch which outputs the attention weight for each channel, and it emphasizes the important ones by specifying a large value for it. Together with the model parameters, the attention weight helps mining the hyperspectral data for the accurate prediction. © 2023 IEEE.

关键词： Bathymetry

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Multimodal-CanGen: A Multimodal Genome-Based Model for Cancer Type Classification 17

Multimodal-CanGen: A Multimodal Genome-Based Model for Cance...

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

作者： Xuan, Zitao Jiang, Xuefeng Shi, Yu Li, Zhide Dong, Yuhan Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Shenzhen Polytechnic University Shenzhen China Fudan University State Key Laboratory of Integrated Chips and Systems Shanghai China

ISBN: (纸本)9798331507398

Cancer type classification is crucial for planning cancer treatment. Previous research has shown that genomic information is highly informative for classifying cancer types. However, the relationship between genomic features and cancer remains highly complex, and current genome-based models primarily use binary genome-level features or rely on comprehensive whole-genome sequencing (WGS) data, which limits their clinical feasibility. To address these complexities and improve clinical applicability, we introduce Multimodal-CanGen, which enhances cancer type classification using clinically accessible cancer ge-nomic sequencing data. Our approach includes a sequence-level encoder employing pre-trained models and graph neural networks (GNNs), a genome-level feature encoder, and a multimodal fusion module that integrates gated attention mechanisms and tensor fusion. The integration of sequence-level features provides insights typically unavailable from binary genome-level features alone, demonstrating superior performance and showing potential for predicting Cancer of Unknown Primary (CUP) cases. Overall, our work provides an advancement in accurately predicting cancer types in clinical practice and has the potential to pave the way for improved cancer treatment. © 2024 IEEE.

关键词： Lung cancer

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Deep Learning-Based Prediction of Short-Term CO2 Emissions in Ireland 17

Deep Learning-Based Prediction of Short-Term CO2 Emissions i...

引用

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

作者： Zhang, Yiteng Pakrashi, Arjun Dev, Soumyabrata The ADAPT SFI Research Centre Dublin Ireland School of Computer Science University College Dublin Ireland Beijing-Dublin International College Beijing China

ISBN: (纸本)9798331507398

Accurate forecasting of carbon dioxide (C02) emissions is crucial for developing effective environmental policies and emission reduction strategies in response to global climate change. This study explores the use of Long Short-Term Memory (LSTM) networks, a type of deep learning model specifically designed for time series data, to predict short-term CO2 emissions in Ireland. Daily CO2 data from a monitoring station in Galway were used to train the LSTM model. Experimental results demonstrate that LSTM outperforms traditional machine learning methods, especially for medium to long-term forecasts. The application of regularization techniques, including Dropout and Early Stopping, further enhanced the model's performance by mitigating overfitting. Future work will involve the use of Bidirectional LSTM (BiLSTM) models and the comparative analysis of other greenhouse gases to improve forecasting accuracy and deepen the understanding of emission dynamics. © 2024 IEEE.

关键词： Low emission

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Deep Spatial Spectral Convolutional Sparse Coding for Spectral CT image Reconstruction 17

Deep Spatial Spectral Convolutional Sparse Coding for Spectr...

引用

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

作者： Liu, Jin Xie, Jingjing Wang, Kun Qiang, Jun College of Computer and Information Anhui Polytechnic University Wuhu China Southeast University Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China

ISBN: (纸本)9798331507398

Spectral computed tomography (CT) provides the potential to generate attenuation maps at varying spectral bins, which can subsequently be employed for the resolve of tissue materials. However, the majority of reconstruction algorithms employed for every projection energy bin typically exhibit a considerable degree of noise. Recently, a series of techniques have been designed in spectral CT reconstruction based on traditional iterative models or deep learning (DL) methods. However, these works are independent or simply coupled, often neglecting the dependency relationships of spatial and spectrum. Additionally, the interpretability and generalization capabilities remain as formidable challenges for current methods. To effectively address these challenges, we initially introduce a spatial-spectral convolutional sparse coding (SS-CSC) framework, which aims to jointly represent spatial and spectral features in a unified manner. Subsequently, we devise a novel deep unfolding network, inspired by SS-CSC, for spectral CT image representation. We refer to this component as the SS-CSC module. Furthermore, we expand the iterative reconstruction scheme for constructing an interpretable deep SS-CSC iterative reconstruction network (SS-CSC-Net) model for spectral CT imaging. The SS-CSC-Net is composed of several iteration blocks, with each block containing two modules: image reconstruction and SS-CSC modules. The spectral CT image is updated using a deep learning-based spatial-spectral prior constraint. Experimental results from both qualitative results and quantitative analyses indicate that the proposed SS-CSC-Net excels in noise reduction and in maintaining tissue edge integrity, delivering superior overall performance. © 2024 IEEE.

关键词： image reconstruction

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Finger Vein Recognition Based on the Improved ViT Model 16

Finger Vein Recognition Based on the Improved ViT Model

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

作者： Pan, Yu Liu, Tingting Southwest University of Science and Technology School of Information Engineering Mianyang China

ISBN: (纸本)9798350330755

The finger vein recognition is generally implemented with the Convolutional Neural Networks (CNN) model, but its accuracy is a little low. To solve the problem, an Improved Vision Transformer for Finger-vein Recognition (IVTFR) model is proposed in this paper, which not only improves the accuracy of the recognition but also lowers training difficulty. The model employs a multi-branch structural re-parameterization convolution block to introduce the inductive biases and enrich the feature vector. Besides, it also incorporates local self-attention to alleviate the smooth distribution of attention scores. Finally, sequence pooling is used to retain relevant information from different parts of the image and reduce the computational complexity. The proposed model has been experimentally validated on public datasets including SDUMLA, THU-FV, FV-USM, and MMCBNU-6000, obtaining accuracy of 95.18%, 99.94%, 99.59%, and 99.83% respectively. Experimental results demonstrate that compared with the original ViT model, the proposed model requires less data and shorter training time, but has higher accuracy, demonstrating the effectiveness of the proposed model. © 2023 IEEE.

关键词： Neural network models

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A 3-DOFs Haptic Feedback Device Equipped Upon Fingertips 17

A 3-DOFs Haptic Feedback Device Equipped Upon Fingertips

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

作者： Liu, Mengqi Guo, Yafei Li, Ke Institute of Intelligent Medial Engineering School of Control Science and Engineering Shandong University Laboratory of Rehabilitation Engineering Jinan China Imperial College London Department of Life Science United Kingdom

ISBN: (纸本)9798331507398

In virtual worlds and robotic teleoperation scenarios, this sense of touch must be artificially reconstructed by stimulation of the human body (usually by the hand) to produce the haptic features required for enhanced realism and human performance. With the development of virtual reality and augmented reality technologies, interacting directly with virtual objects with haptic feedback is receiving more and more attention. In this study, a three degree of freedoms (3-DOFs) haptic feedback device was proposed, which can generate a force haptic feedback by skin stretching at the fingertips. The device employed a slider-crank linkage mechanism and a curved- W -shaped five-bar linkage mechanism, with the motor driving the slider-crank linkage mechanism mounted on the back of the finger and the two motors driving the curved- W-shaped five-bar linkage mechanism mounted on both sides of the finger. In addition, this study also analyzes the kinematics and statics of the structure of the proposed device, which provides the actual value of pressure 2.92 N slightly larger than the theoretical value of 2.67 N. It also verifies the reasonableness of the design by analyzing the results and experiments, and the experimental accuracy of the direction recognition gradually becomes larger with the increasing of the value of the condition. This study can contribute to the understanding of human multimodal tactile perception mechanisms on the one hand, and provide motivation for robotics and haptics on the other. © 2024 IEEE.

关键词： Robotics

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A Real-Time GPU-Based Framework for Curved Array Ultrafast Ultrasound Doppler Imaging 17

A Real-Time GPU-Based Framework for Curved Array Ultrafast U...

引用

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

作者： Wang, Baoyu Miao, Yuhan Cao, Lei Liu, Chengan Zhang, Shi College of Computer Science and Engineering Northeastern University Liaoning Province Shenyang China Neusoft Medical Systems Corporation Liaoning Province Shenyang China

ISBN: (纸本)9798331507398

Doppler flow imaging is a hybrid imaging method with 2D and Doppler images. The traditional dual-type segment scanning sequence has limited real-time display frame rate and ensemble size. Ultrafast Doppler imaging improves sensitivity to low-velocity microfluidic by providing a higher frame rate and relieving the problem of limited ensemble size. However, the uniform-type continuous scanning sequence, which uses the same echo data to obtain the two-mode images separately, ensures a high frame rate but at the expense of 2D imaging quality. We propose a dual-type mixed continuous scanning sequence, where each combined frame consists of one or more BW mode lines and a complete Color mode frame, and both BW mode and Color mode emission conditions can be configured individually. The sequence maintains the continuous acquisition of micro-vessels for a long period while outputting high-resolution BW mode tissue images, and improves the sensitivity to micro-vessels. Meanwhile, this paper proposes a GPU dual-rate processing framework suitable for this sequence, which can synchronize the acquisition results at 750 Hz and 150 Hz frame rates, and provide both power Doppler images sensitive to low-speed blood flow and correct velocity Doppler images for high-speed blood flow. This GPU processing framework offers the possibility of observing different velocity blood flows in the same scene in clinical ultrasound diagnosis. To verify the performance of the proposed method, in vivo experiments were performed on the renal and liver of a healthy volunteer. The results demonstrate that the framework can enhance the visualization of micro-vessels and support real-time velocity analysis of low-velocity blood flow in the abdomen using the convex array. © 2024 IEEE.

关键词： Ultrasonic imaging

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Environmental Sound Classification Based on Multi-channel Features and Hybrid Attention 17

Environmental Sound Classification Based on Multi-channel Fe...

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

作者： Liu, Guishan Zhou, Shuai Peng, Zhangjun Li, Li School of Computer Science and Technology Southwest University of Science and Technology Sichuan Mianyang621000 China College of Mechanical and Electrical Engineering Sichuan Electronic Machinery Vocational and Technical College Sichuan Mianyang621000 China Sichuan Autonomous Controllable Artificial Intelligence Engineering Technology Center Sichuan Mianyang621000 China

ISBN: (纸本)9798331507398

Environmental sound classification (ESC) is an important research direction, but it remains challenging due to the wide variety of environmental sounds which are difficult to uniformly represent and can be easily interfered by noise. The ESC has limitations in accurately separating and recognizing sounds in multi-class complex scenarios, especially under conditions of noise interference or overlapping sound sources. To improve the accuracy of ESC recognition, this paper proposes a classification method that utilizes a multi-channel feature and a mixed attention model. Firstly, the time-level and frequency-domain signals of the ESC are extracted, and filters are applied to extract spectral features and reconstruct into a feature graph. The limitations of single-feature representation are addressed by leveraging the complementarity between the three-channel features. Secondly, a hybrid classification model composed of channel and time-frequency concentration module classification model is introduced. The channel attention model calculates the feature map and assigns weights to different channels, and the channel features with more effective information and better resolution of this kind of sound can be allocated more weights. The time-frequency concentration module, however, focuses on more effective information in the time domain and frequency domain. The method proposed in this paper can effectively suppress the background noise, eliminate redundancy, and improve the convergence speed and classification accuracy. The recognition accuracies on ESC-10 and ESC-50 datasets reach 96.25% and 89.56% respectively, and 98.40% on the Urbansound8k dataset. © 2024 IEEE.

关键词： Frequency domain analysis

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Detection and Classification of Malaria Parasite Based on Improved YOLOv5 Model 16

Detection and Classification of Malaria Parasite Based on Im...

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

作者： Liu, Zhiming Liu, Hongying Sun, Yimeng East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing Shanghai China Shanghai University of Electric Power School of Electronic and Information Engineering Shanghai China

ISBN: (纸本)9798350330755

At present, malaria is still one of the major threats to human health. Timely diagnosis of malaria is critical to reducing both transmission and mortality. Malaria blood smear microscopy is the standard for malaria detection, but due to the cumbersome steps of manual evaluation, this diagnostic method is time-consuming and prone to missed and false positives, even in the hands of experienced physicians. Deep learning-based neural network models have achieved great success in object detection, but state-of-the-art models have not been widely used in biological image data. In response to this challenge, we have introduced an enhanced model based on YOLOv5 to accurately identify malaria blood smear cells and detect their infected stages. Based on the original YOLOv5, CA module and BiFPN are used. The experimental results demonstrate that the enhanced YOLOv5 model achieves an average accuracy of 79.3% on the public Plasmodium vivax (malaria) infected human blood smear dataset. © 2023 IEEE.

关键词： Object detection

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Postural Synergy Analysis of Continuous Hand Movements for Application to Prosthetic Hands 17

Postural Synergy Analysis of Continuous Hand Movements for A...

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

作者： Jiang, Hai Yamanoi, Yusuke Chen, Peiji Yong, Xu Li, Guanglin Yokoi, Hiroshi Jing, Xiaobei Shenzhen518055 China The Univiersity of Electro-Communications Department of Mechanical Engineering and Intelligent Systems Tokyo 182-8585 Japan Tokyo University of Science Faculty of Engineering Department of Elecrical Engineering Tokyo 125-8585 Japan

ISBN: (纸本)9798331507398

This paper focuses on exploring the coordinated movements used to control dexterous myoelectric prosthetic hands by hand motion analysis inspired by the motor synergy theory of the human hand. Recently, prosthetic hands have become multi-degree-of-freedom, and simultaneous proportional control of each finger is the most required challenge. In response to this problem, motor synergy is expected to play a key role in constructing the control method because it reduces the amount of information on the control object. In the experiment, we used a Leap Motion Controller to measure 60 continuous hand movement data from four healthy individuals, analyzed based on principal component analysis methods, and extracted several principal component data used by humans to control various grasping movements. We produced a hand model with 19 degrees of freedom for visualizing the principal component data. The results of the visualization showed similarities in postural synergies among the four healthy individuals used to control continuous hand movements. Based on the acquired synergies, the number of learning data for the control method of myoelectric prosthetic hands will be greatly reduced. © 2024 IEEE.

关键词： Degrees of freedom (mechanics)

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