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Existence and Non-Existence of Solutions to Some Degenerate Coercivity Quasilinear Elliptic Equations with Measure data

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Journal of Mathematical Research with applications 2022年第2期42卷 173-188页

作者： Maoji RI Xiangrui LI Qiaoyu TIAN Shuibo HUANG School of Mathematics and Computer Science Northwest Minzu University Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University

In this article,we study the existence and non-existence of weak solutions to the following quasilinear elliptic problem with principal part having degenerate coercivity and nonlinear term involving gradie,■where??RN... 详细信息

关键词： elliptic equation degenerate coercivity measures data existence non-existence

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Automatic Segmentation of Metastasis Lesions in SPECT Bone Scan Images

Automatic Segmentation of Metastasis Lesions in SPECT Bone S...

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2020 International Conference on Intelligent computing, Automation and Systems, ICICAS 2020

作者： Gao, Ruiting Lin, Qiang Luo, Mingyang Man, Zhengxing Cao, Yongchun Wang, Haijun Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou China Gansu Provincial People's Hospital Department of Nuclear Medicine Lanzhou China

ISBN: (纸本)9781728191461

SPECT bone imaging is among the main nuclear medicine functional imaging modalities, which has the potential of early diagnosis of various serious diseases such as cancers. However, the low resolution and contrast of SPECT bone images bring huge challenges to performance on segmenting hotspots or lesions in SPECT bone scan images. In this work, three different algorithms including K-means clustering method, region growth method and C-V model are introduced to segment lesions in SPECT bone scan images by fine-tuning parameters, focusing on the bone metastatic lesions area in SPECT imaging. Specifically, each of the original 256×1024 whole body images is cropped into a 256×256 thoracic region including ribs and spine, followed by the data normalization. Then, different parameters are experimentally determined for the above three algorithms, leading to three different segmentation algorithms. Last, experimental evaluation conducted on a group of real-world samples of SPECT bone scan images reveals that our methods are workable for segmenting lesions with SPECT imaging, obtaining a value of 0.7307, 0.7768 and 0.8076 for Tanimoto similarity coefficient metric by the three methods, respectively. Particularly, the C-V model based segmentation method is able to provide more assistive information for oncologists on diagnose of tumors and other related diseases. © 2020 IEEE.

关键词： Image segmentation

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Automatic lesion segmentation of metastases in SPECT images using U-Net-based model 2

Automatic lesion segmentation of metastases in SPECT images ...

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2nd International Conference on Signal Image Processing and Communication, ICSIPC 2022

作者： Gao, Runxia Lin, Qiang Man, Zhengxing Cao, Yongchun Laboratory of Chinese National Language and Information Technology Ministry of Education Northwest Minzu University Gansu Lanzhou730030 China Laboratory of Streaming Data Computing and Applications Northwest Minzu University Gansu Lanzhou730030 China

ISBN: (纸本)9781510657694

SPECT image is a commonly used nuclear medicine imaging instrument and plays a very important role in the imaging diagnosis of bone metastasis disease. As lung cancer is one of the malignant tumors that endanger human life and health, early detection and treatment is the only way to delay the life cycle of this disease. To accurately segment lung cancer bone metastases from SPECT images, different segmentation models oriented to the reliable assessment of bone metastases were studied and constructed in this paper. First, data pre-processing, such as normalization, is performed on SPECT bone metastasis images. Then, improved image segmentation models are constructed based on the U-Net model, including the U-Net model that fuses the residual structure and the U-Net model that fuses both the residual structure and the attention mechanism;finally, experiments were conducted on a set of real clinical SPECT nuclear medicine data, and the multigroup experimental demonstration showed that the U-Net model incorporating residual structure and attention mechanism proposed in this paper could better segment the bone metastases in the thoracic region of SPECT images, and the obtained pixel accuracy PA, category average pixel accuracy MPA, and average cross-merge ratio MIoU were 0.9955, 0.7824, and 0.7291, respectively. © 2022 SPIE.

关键词： Image segmentation

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Classification of abnormal behavior in the elderly based on Wi-Fi

Classification of abnormal behavior in the elderly based on ...

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2021 International Symposium on Computer Technology and Information Science, ISCTIS 2021

作者： Weiqiong, Wang Yongchun, Cao Qiang, Lin Li, Yifan Northwest Minzu University School of Mathematics and Computer Science Lanzhou China Key Laboratory of Streaming Data Computing and Application Northwest Minzu University Lanzhou China

ISBN: (纸本)9781665414418

Abnormal behavior in daily life is a great threat to the health of the elderly, so it is of great significance to detect the abnormal behavior of the elderly. Using ubiquitous commercial Wi-Fi devices to collect information, behavior detection can be performed in an indoor environment without carrying other devices. The Wi-Fi based abnormal behavior detection for the elderly proposed in this paper is aimed at the fall and wandering behaviors that the elderly are prone to. At the same time, squatting and sitting behaviors those are similar to the fall behaviors are distinguished. Several classical classification models are used to compare the effect and recognition time. The experimental comparison shows that the SVM and KNN machine learning methods after adjusting the model parameters can achieve the same recognition effect as the deep learning model LSTM, shorten the running time and improve the efficiency. © 2021 IEEE.

关键词： Support vector machines

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Research on the Recognition of Abnormal Behaviors in the Elderly Based on Wi-Fi Signals 4

Research on the Recognition of Abnormal Behaviors in the Eld...

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2021 4th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2021

作者： Li, Yifan Cao, Yongchun Lin, Qiang Wang, Weiqiong School of Mathematics and Computer Science Northwest Minzu University Lanzhou Gansu730030 China Key Laboratory of Streaming Computing and Applications Northwest Minzu University Lanzhou Gansu730030 China

Wandering behaviour is an important diagnostic indicator for patients with Alzheimer's disease, and falls are the main threat to the health of the elderly. Effective detection of these two types of abnormal behaviours of the elderly and timely intervention are important for improving the quality of life of patients with Alzheimer's disease and related diseases significance. This study proposes and studies a method for detecting and identifying abnormal behaviours of the elderly based on Wi-Fi signals. First, the correlation between Wi-Fi signal changes and abnormal behaviours of the elderly was analyzed;secondly, this study applied general Wi-Fi equipment to obtain the status data of the abnormal behaviours of the elderly and preprocess the data through Hampel filtering;then, the effective features of the CSI sequence fragments were designed and extracted, a data set of 2243 samples including 7 types of abnormal behaviours of the elderly was constructed;finally, a BiLSTM-Based abnormal behaviour recognition model for the elderly was constructed, and the average classification accuracy reached 95.95%, which proved this experiment is feasible and effective. © Published under licence by IOP Publishing Ltd.

关键词： Patient treatment

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Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science

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Brain Informatics 2020年第1期7卷 16页

作者： Li, Rui Zhang, Junsong National Engineering Laboratory for Educational Big Data Central China Normal University WuhanHubei China Fujian Key Laboratory of Brain-Inspired Computing Technique and Applications Department of Artificial Intelligence School of Informatics Xiamen University Xiamen China

The mystery of aesthetics attracts scientists from various research fields. The topic of aesthetics, in combination with other disciplines such as neuroscience and computer science, has brought out the burgeoning fields of neuroaesthetics and computational aesthetics within less than two decades. Despite profound findings are carried out by experimental approaches in neuroaesthetics and by machine learning algorithms in computational neuroaesthetics, these two fields cannot be easily combined to benefit from each other and findings from each field are isolated. Computational neuroaesthetics, which inherits computational approaches from computational aesthetics and experimental approaches from neuroaesthetics, seems to be promising to bridge the gap between neuroaesthetics and computational aesthetics. Here, we review theoretical models and neuroimaging findings about brain activity in neuroaesthetics. Then machine learning algorithms and computational models in computational aesthetics are enumerated. Finally, we introduce studies in computational neuroaesthetics which combine computational models with neuroimaging data to analyze brain connectivity during aesthetic appreciation or give a prediction on aesthetic preference. This paper outlines the rich potential for computational neuroaesthetics to take advantages from both neuroaesthetics and computational aesthetics. We conclude by discussing some of the challenges and potential prospects in computational neuroaesthetics, and highlight issues for future consideration. © 2020, The Author(s).

关键词： Brain

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Classifying SPECT Bone Metastasis Images in Grayscale Format with VGGNets 3

Classifying SPECT Bone Metastasis Images in Grayscale Format...

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3rd IEEE International Conference on Communications, Information System and Computer Engineering, CISCE 2021

作者： Zhao, Shaofang Chen, Xiaoyan Zheng, Ziwen Lin, Qang School of Mathematics and Computer Sciece Northwest Minzu University Gansu Lanzhou730030 China Key Laboratory of Chinese National Language and Information Technology Ministry of Education Northwest Minzu University Gansu Lanzhou730030 China Key Laboratory of Streaming Data Computing and Applications Northwest Minzu University Gansu Lanzhou730030 China

ISBN: (纸本)9780738112152

For the purpose of accurate diagnosis of bone metastasis diseases with nuclear medicine SPECT imaging technology in this work, we propose to study and construct CNN based classification model for identifying bone metastases with real-world SPECT imaging data. First, the geometric transformation including image mirroring, rotation, and translation is used to augment dataset of SPECT bone scans. Second, the standard VGG model is adopted to develop classifier of SPECT images. Lastly, a set of real-world data of whole-body SPECT bone scans is used to evaluate the built model, obtaining a best value of 0.996, 0.995, 0.997 and 0.995 for accuracy Acc, precision Pre, recall Rec and F-1 score are, respectively. © 2021 IEEE.

关键词： Image classification

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Classifying Bone Metastasized SPECT Images in RGB Format with VGGNets 3

Classifying Bone Metastasized SPECT Images in RGB Format wit...

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3rd IEEE International Conference on Communications, Information System and Computer Engineering, CISCE 2021

作者： Chen, Xiaoyan Zheng, Ziwen Liu, Liangxia Wang, Weili Lin, Qiang Key Laboratory of China's Ethnic Languages and Information Technology Ministry of Education Northwest Minzu University Gansu Lanzhou730000 China School of Mathematics and Computer Sciece Northwest Minzu University Gansu Lanzhou730030 China Key Laboratory of Streaming Data Computing and Applications Northwest Minzu University Gansu Lanzhou730030 China

ISBN: (纸本)9780738112152

SPECT imaging is widely used for displaying lesions of bone metastasis in nuclear medicine. With SPECT images in RGB format, in this work, we propose to develop automated classification models based on VGG framework, which can answer that whether or not an image contain lesions. Experimental evaluation conducted on a set of real-world data of SPECT imaging shows that the VGG13 model can effectively classify bone metastasis SPECT images, obtaining a value of 0.992, 0.99, 0.99 and 0.95 for accuracy Acc, precision Pre, recall Rec and F-1 scores, respectively. © 2021 IEEE.

关键词： Nuclear medicine

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Research on Arthritis Detection Method for SPECT Nuclear Medicine Bone Imaging 3

Research on Arthritis Detection Method for SPECT Nuclear Med...

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3rd IEEE International Conference on Communications, Information System and Computer Engineering, CISCE 2021

作者： Wang, Weili Cao, Yongchun Lin, Qiang Man, Zhengxing Li, Tongtong Key Laboratory of China's Ethnic Languages and Information Technology Ministry of Education Northwest Minzu University Gansu Lanzhou730000 China School of Mathematics and Computer Sciece Northwest Minzu University Gansu Lanzhou730030 China Key Laboratory of Streaming Data Computing and Applications Northwest Minzu University Gansu Lanzhou730030 China

ISBN: (纸本)9780738112152

SPECT bone imaging is an effective method for early diagnosis of bone metastases from malignant tumors. In addition to bone metastases, arthritis and other diseases will also appear as abnormal metabolic distribution areas in SPECT images. Early diagnosis of arthritis based on SPECT bone imaging is of great significance for the treatment of patients. This article takes SPECT bone imaging as the object to study and construct an arthritis disease classifier based on convolutional neural network. Firstly, data preprocessing is carried out to deal with the impact of small amount of medical image data and individual differences between imaging data on the classification effect of deep learning model. Secondly, a classifier suitable for low resolution and large-scale SPECT images is constructed based on VGGNet. Finally, based on a set of real SPECT bone imaging data sets to compare the arthritis diagnosis results of different depth classification models. The experimental results show that the classifier constructed in this paper can effectively detect arthritis diseases, and the accuracy, recall and F-1 values are 0.9309, 0.9511 and 0.9284, respectively. © 2021 IEEE.

关键词： Diseases

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CNN-Based Classifier for Diagnosing SPECT Bone Images 3

CNN-Based Classifier for Diagnosing SPECT Bone Images

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3rd IEEE International Conference on Communications, Information System and Computer Engineering, CISCE 2021

作者： Guo, Yanru Chen, Xiaoyan Zheng, Ziwen Lin, Qiang School of Mathematics and Computer Sciece Northwest Minzu University Gansu Lanzhou730030 China Key Laboratory of Chinese National Language and Information Technology Ministry of Education Northwest Minzu University Gansu Lanzhou730030 China Key Laboratory of Streaming Data Computing and Applications Northwest Minzu University Gansu Lanzhou730030 China

ISBN: (纸本)9780738112152

To assist nuclear medicine doctors for quickly and accurately identifying bone metastases, in this work, we develop a convolutional neural network to automatically classify whole-body SPECT bone scans. First, the original data of SPECT bone scans are augmented by using geometric transformation. Second, two deep model VGG5 and VGG7 are developed by following the famous VGG model, where a 2\times 1 maximum pooling is introduced before the first layer convolution. Experimental evaluation conducted on real-world SPECT images shows that the developed models are workable to classify SPECT images into categories of concern. © 2021 IEEE.

关键词： Convolutional neural networks

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