检索结果-内蒙古大学图书馆

ConDANet: Contourlet Driven Attention network for Automatic Nuclei Segmentation in Histopathology Images

IEEE ACCESS 2023年 11卷 129321-129330页

作者： Imtiaz, Tamjid Fattah, Shaikh Anowarul Saquib, Mohammad Univ Penn Dept Bioengn Philadelphia PA 19104 USA Bangladesh Univ Engn & Technol Dept Elect & Elect Engn Dhaka 1205 Bangladesh Univ Texas Dallas Dept Elect Engn Richardson TX 75083 USA

Cell nuclei segmentation, the task of identifying the precise boundary of the nucleus in each cell in a histopathology image, is a rudimentary task prior to single-cell analysis. While addressing this task, two of the major challenges are the precise delineation of the small-shaped nucleus structure and the characterization of the edge region of the nucleus for both the regular and deformed nucleus. To overcome these challenges, a contourlet driven attention network, namely ConDANet, is developed which incorporates a novel attention mechanism along with a content-preserving sampling strategy. Instead of using conventional spatial or channel-wise attention, a controlling signal driven attention mechanism is proposed, which is capable of extracting fine edge details of the nuclei regions by focusing on the relevant edge or boundary information of the nuclei structures. The contourlet transform based controlling signal generation scheme is proposed, which not only exploits the advantage of the multi-scale time frequency localization properties of wavelets but also provides a high degree of directionality. Additionally, the wavelet pooling strategy is incorporated to the network which preserves the textural content of the nucleus in histopathology images and prevents the loss of information in the subsequent sampling operation of the encoder-decoder part of the network. Finally, the proposed method is employed for analyzing three publicly available histopathology datasets to manifest its effectiveness in segmenting nuclei from cellular images extracted from a wide variety of organs and patients. Dice scores obtained in these three datasets are 88.9%, 81.71%, and 75.12% which are found to be superior compared to some state-of-the-art methods.

关键词： Computer architecture Image segmentation Histopathology Decoding Task analysis Image edge detection Image color analysis Nucleus segmentation attention mechanism contourlet transform wavelet pooling encoder-decoder network

来源：评论

学校读者我要写书评

暂无评论

Lightweight Asymmetric Dilation network for Real-Time Semantic Segmentation

引用

IEEE ACCESS 2021年 9卷 55630-55643页

作者： Hu, Xuegang Gong, Yu Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400000 Peoples R China Chongqing Univ Posts & Telecommun Lab Intelligent Anal & Decis Complex Syst Chongqing 400000 Peoples R China

Semantic segmentation is a very important and challenging problem in computer vision. Many applications, such as automated driving and robotic navigation in urban road scenes, require accurate and efficient segmentation. Nowadays, system models are often designed with high speed but a large number of parameters, or they take up a lot of memory space with a very small speed, so they are not suitable for real-time semantic segmentation conditions. In order to solve this problem, we propose a more comprehensive model that has not only a faster speed, but also a smaller number of parameters and a higher accuracy which is termed as Lightweight Asymmetric Dilation network (LADNet). Our model is based on our Lightweight Asymmetric Dilation Module (LAD Module) which provides a larger receptive field than all existing lightweight models to learn more information, while Lightweight Asymmetric Dilation-A (LAD-A) can better perceive spatial and semantic information, and Lightweight Asymmetric Dilation-B (LAD-B) can better perceive semantic information. Our Lightweight Downsampling Module (LDM) downsamples the feature map, it can greatly reduce model parameters. Finally, our Attention Enhancement decoder (AED) to restore the feature map to the same size as the resolution of the original image, AED enables two attentional feature maps to simultaneously guide semantic information for better semantic segmentation of images. Our extensive experiments on the Cityscapes, CamVid, and NYUv2 test set show that our model is able to achieve the best balance in parameters, accuracy, and speed.

关键词： Semantics Convolution Real-time systems Image segmentation Task analysis Kernel Feature extraction Attention mechanism convolutional neural network encoder-decoder network lightweight model real-time semantic segmentation

来源：评论

学校读者我要写书评

暂无评论

DUSFormer: Dual-Swin Transformer V2 Aggregate network for Polyp Segmentation

引用

IEEE ACCESS 2024年 12卷 8822-8832页

作者： Xia, Zhangrun Chen, Jingliang Lu, Chengzhun Hubei Univ Technol Sch Comp Sci Wuhan 430000 Peoples R China

The convolutional neural network method has certain limitations in medical image segmentation. As a result of the limited availability of polyp datasets, the model framework is vulnerable to instability and overfitting during training. Beyond that, ambiguous target boundaries can make segmentation more difficult. We propose a Dual-Swin Transformer V2 Aggregate network called DUSFormer in order to address these issues, which can be used to more accurately capture spatial semantic features with different complexities. Specifically, the DUSFormer network consists of two encoders and decoders for progressive feature extraction and deep feature extraction. The decoder uses the Stepwise Feature Fusion (SFF) module to locally emphasize and fuse the feature maps at various levels. This architecture enables faster and more efficient dissemination of feature information at all levels, enabling faster integration of features with global dependencies and local details. In addition, an Adaptive Correction Module (ACM) is introduced to construct an aggregation relationship of edge information between layers of the encoder and the decoder, which can correct the predictive segmentation with irregular and blurred boundaries, and increase the precision of segmentation. The DUSFormer model has many advantages in terms quantitative and generalization ability on three polyp image segmentation datasets.

关键词： Medical segmentation encoder-decoder network deep learning Swin Transformers V2 transformers

来源：评论

学校读者我要写书评

暂无评论

Enhancing particle string detection in electrorheological plasmas using asymmetrical kernel convolutional networks

引用

MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2024年第2期5卷 025050页

作者： Klein, M. Dormagen, N. Dietz, C. Thoma, M. H. Schwarz, M. Justus Liebig Univ Inst Phys 1 D-35392 Giessen Germany THM Univ Appl Sci NanoP D-35390 Giessen Germany

Under different plasma conditions and electric fields in a complex plasma the plasma particles organize themselves in a string-like or chain-like manner. A phase transition from string-like to an isotropic particle distribution is observed at different electrical conditions. The streaming of charged ions around plasma particles with the surrounding electric field gives the plasma its electrorheological properties. The visibility of individual particles in a complex plasma opens up the opportunity to examine properties and phase transitions of such electrorheological fluids in detail. Because of the limited one-dimensional symmetry, determining the configuration of a particle and recognizing strings in particle distributions is not always straightforward. Several approaches have already been used to analyse particle clouds while either considering each particle locally or considering the particle cloud as a whole without providing information about single particle configurations. This paper presents a new machine learning approach that takes advantage of particle distributions over the entire particle cloud and detects all string-like particles at once, using a convolutional neural network in form of an encoder-decoder network with asymmetric kernel convolutions. This not only enhances the result quality but also accelerates the evaluation process, possibly enabling real-time analyses on electrorheological phase transitions, while achieving an accuracy of over 95% on manually labelled data.

关键词： complex plasma electrorheology convolutional neural network encoder-decoder network

来源：评论

学校读者我要写书评

暂无评论

Wind power prediction based on periodic characteristic decomposition and multi-layer attention network

引用

NEUROCOMPUTING 2023年 534卷 119-132页

作者： Liao, Xuechao Liu, Zhenxing Zheng, Xiujuan Ping, Zuowei He, Xin Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430080 Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Rea Wuhan 430080 Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430080 Peoples R China Naval Univ Engn Natl Key Lab Sci & Technol Vessel Integrated Power Wuhan 430030 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China

Aiming at the wind power characteristics of temporality, periodicity and complexity, the periodic law of short-term and long-term repetitive patterns is studied, and an integrated dual-channel prediction model is proposed. A practical periodic characteristic extracting strategy is designed to show the hidden peri-odic law of the original signal. Combining the grid search algorithm with the variation trend of ampli-tude/period, the optimal periodic step is determined. Based on the above analysis, the original signal is decomposed into temporal and periodic components. Then the temporal attention network and the encoder-decoder attention network are schemed out to dispose the two components respectively. Finally, the linear regression attention network is adopted to realize data fitting. The integrated forecast-ing framework can deal with the long-term and short-term dependencies of the original data at the same time, and ensure the rapid convergence of training process, thereby improve the prediction accuracy and stability. The multi-dimensional experimental verification is carried out through the comparison of eval-uation indicators, prediction trends, scatter plots and box plots.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Wind power prediction Periodic feature extraction Seasonal and trend decomposition Temporal pattern attention encoder-decoder network

来源：评论

学校读者我要写书评

暂无评论

Joint pyramid attention network for real-time semantic segmentation of urban scenes

引用

APPLIED INTELLIGENCE 2022年第1期52卷 580-594页

作者： Hu, Xuegang Jing, Liyuan Sehar, Uroosa Chongqing Univ Posts & Telecommun Key Lab Intelligent Anal & Decis Complex Syst Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Multimedia Commun Res Lab Chongqing 400065 Peoples R China Northeastern Univ Cross Media Artificial Intelligence Lab Shenyang 110000 Peoples R China

Semantic segmentation is an advanced research topic in computer vision and can be regarded as a fundamental technique for image understanding and analysis. However, most of the current semantic segmentation networks only focus on segmentation accuracy while ignoring the requirements for high processing speed and low computational complexity in mobile terminal fields such as autonomous driving systems, drone applications, and fingerprint recognition systems. Aiming at the problems that the current semantic segmentation task are facing, it is difficult to meet the actual industrial needs due to its high computational cost. We propose a joint pyramid attention network (JPANet) for real-time semantic segmentation. First, we propose a joint feature pyramid (JFP) module, which can combine multiple network stages with learning multi-scale feature representations with strong semantic information, hence improving pixel classification performance. Second, we built a spatial detail extraction (SDE) module to capture the shallow network multi-level local features and make up for the geometric information lost in the down-sampling stage. Finally, we design a bilateral feature fusion (BFF) module, which properly integrates spatial information and semantic information through a hybrid attention mechanism in spatial dimensions and channel dimensions, making full use of the correspondence between high-level features and low-level features. We conducted a series of experiments on two challenging urban road scene datasets (Cityscapes and CamVid) and achieved excellent results. Among them, the experimental results on the Cityscapes dataset show that for 512 x 1024 high-resolution images, our method achieves 71.62% Mean Intersection over Union (mIoU) with 109.9 frames per second (FPS) on a single 1080Ti GPU.

关键词： Attention mechanism encoder-decoder network Feature pyramid module Lightweight network Real-time semantic segmentation

来源：评论

学校读者我要写书评

暂无评论

A deep learning based surrogate model for reservoir dynamic performance prediction

GEOENERGY SCIENCE AND ENGINEERING

引用

GEOENERGY SCIENCE AND ENGINEERING 2024年 233卷

作者： Wang, Sen Xiang, Jie Wang, Xiao Feng, Qihong Yang, Yong Cao, Xiaopeng Hou, Lei Minist Educ Key Lab Unconvent Oil & Gas Dev Qingdao 266580 Peoples R China China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China CNOOC China Ltd Shenzhen Nanhai East Petr Res Inst Shenzhen 518000 Peoples R China Shandong Inst Petr & Chem Technol Dongying 257061 Peoples R China SINOPEC Shengli Oilfield Co Dongying 257015 Peoples R China Shanghai Jiao Tong Univ China UK Low Carbon Coll Shanghai 201306 Peoples R China

Predicting the reservoir dynamic performance is an important basis for exploiting the remaining oil and improving the recovery. Traditional prediction techniques primarily include numerical simulation and reservoir engineering methods, which show technical drawbacks such as low computational efficiency and too simplified assumptions. To tackle this challenge, we propose a surrogate model for reservoir dynamic performance prediction based on a Variety of View deep convolutional encoder-decoder (VoV-DCED) network. The influence of production time is accounted for using dimension expansion. Our model directly predicts the remaining oil saturation distribution with known reservoir permeability maps and varying well schedules. We examine the performance of our model in 2D and 3D waterflooding reservoirs, which shows that the prediction results are in good consistence with the reservoir numerical simulation. The model can be extended to account for the impacts of other factors, such as initial oil saturation distribution, well patterns, and non-frequent heterogeneities. Although deep learning takes a longer time for dataset preparation in comparison to the traditional simulation, the prediction efficiency of our trained model is almost two orders of magnitude higher than that of numerical simulation. Therefore, this surrogate model can act as an efficient tool for automatic history matching and production optimization-which require multiple simulation runs.

关键词： Deep learning Performance prediction encoder-decoder network Surrogate model Well schedule Water-flooding

来源：评论

学校读者我要写书评

暂无评论

ConvMixer-based encoder and classification-based decoder architecture for breast lesion segmentation in ultrasound images

引用

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 89卷

作者： Uzen, Huseyin Bingol Univ Fac Engn Dept Comp Engn Bingol Turkiye

Automatic breast lesion segmentation in ultrasound images is an important research topic, as breast cancer is one of the most common and dangerous cancers. However, lesion segmentation is a difficult task due to the challenges encountered in ultrasound images. In this study, a new encoder-decoder network based on ConvMixer is designed for breast lesion segmentation in ultrasound images. This model, called the ConvMixer-based encoderClassification-Based decoder (CE-CD), divides the pixel-level segmentation task into image-level classification and pixel-level detection, effectively combining them. ConvMixer and DenseNet121 are used in the encoder. While spatial and semantic details are obtained with DenseNet121, long-range-context details are obtained with ConvMixer. Then, these features are combined and transferred to the decoder. In addition, the decoder consists of a classification network and a detection network. The detection network obtains the lesion detection score at the pixel level, while the classification network obtains the lesion classification score at the image level. In the last section of CE-CD, the detected lesion class is determined using the classification output with the result generation algorithm. The BUSI dataset was used to analyze the performance of the CE-CD. As a result of experimental studies, the proposed model provided a superior performance than the state-of-the-art models with a Jaccard score of 69.23% and a Dice score of 80.23%. Furthermore, using ConvMixer together with DenseNet121 in the analyses performed for CE-CD effectively increased the success. On the other hand, although the mutual exclusion problem was encountered, the proposed decoder was found to be effective.

关键词： Breast ultrasound Breast lesion segmentation ConvMixer Segmentation network encoder-decoder network

来源：评论

学校读者我要写书评

暂无评论

Trustworthy Building Fire Detection Framework With Simulation-Based Learning

引用

IEEE ACCESS 2021年 9卷 55777-55789页

作者： Kim, Young-Jin Kim, Hanjin Lee, Seunggi Kim, Won-Tae Korea Univ Technol & Educ Dept Comp Sci Engn Cheonan 31253 South Korea Korea Univ Technol & Educ Dept Future Convergence Engn Cheonan 31253 South Korea

With the difficulty of collecting desirable training data due to the heterogeneities of IoT sensors in various buildings and the scarcity of fire events, it is time consuming and expensive to apply data-driven deep learning approaches to fire detection systems in specific building environments. Simulation-based learning has been actively researched to mitigate data scarcity problems by reproducing potential fire events. Since simulation-based learning mainly depends on synthetic training data, trained deep learning models may generate erroneous predictions in real-world scenarios that are unlike any of the training samples. In this paper, we propose a trustworthy building fire detection framework based on a multioutput encoder-decoder network, named MEDNet, which is designed for the practical usage of simulation-based learning in building fire detection. The fundamental steps of our approach are (1) modeling and simulating fire events to create realistic synthetic data that reflect data from actual buildings, (2) predicting a fire event and dissimilarities between real input data and synthetic training data based on the trained MEDNet model, and (3) operating a switching mechanism to use a knowledge-based method that does not depend on synthetic training data when dissimilarities exist. Finally, we perform simulation experiments based on a real building compartment where the proposed framework is compared with conventional time-series classification networks on various evaluation datasets. The proposed framework is trustworthy in practical usage because MEDNet with a switching mechanism achieves a 36.65% higher F1-score than conventional time-series classification networks and generates false-positive predictions lower than 0.02% even in unpredictable scenarios.

关键词： Buildings Data models Training data Deep learning Predictive models Internet of Things Atmospheric modeling Simulation-based learning deep learning modeling and simulation encoder-decoder network supervised learning-based rare event detection building fire detection

来源：评论

学校读者我要写书评

暂无评论

Pavement crack detection network based on pyramid structure and attention mechanism

引用

IET IMAGE PROCESSING 2020年第8期14卷 1580-1586页

作者： Xiang, Xuezhi Zhang, Yuqi El Saddik, Abdulmotaleb Harbin Engn Univ Sch Informat & Commun Engn Harbin 150001 Peoples R China Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada

Automatic detection of pavement crack is an important task for conducting road maintenance. However, as an important part of the intelligent transportation system, automatic pavement crack detection is challenging due to the poor continuity of cracks, the different width of cracks, and the low contrast between cracks and the surrounding pavement. This study proposes a novel pavement crack detection method based on an end-to-end trainable deep convolution neural network. The authors build the network using the encoder-decoder architecture and adopt a pyramid module to exploit global context information for the complex topology structures of cracks. Moreover, they introduce a spatial-channel combinational attention module into the encoder-decoder network for refining crack features. Further, the dilated convolution is used to reduce the loss of crack details due to the pooling operation in the encoder network. In addition, they introduce a lovasz hinge loss function, which is suitable for small objects. They train the authors' network on the CRACK500 dataset and evaluate it on three pavement crack datasets. Among the methods they compare, their method can achieve the best experimental results.

关键词： learning (artificial intelligence) object detection crack detection roads structural engineering computing image recognition cracks maintenance engineering convolutional neural nets intelligent transportation systems pavement crack detection network pyramid structure attention mechanism automatic pavement crack detection surrounding pavement spatial-channel combinational attention module crack features CRACK500 dataset pavement crack datasets encoder-decoder network road maintenance encoder-decoder architecture end-to-end trainable deep convolution neural network complex topology structures pooling operation intelligent transportation system

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：