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Creating Knowledge-Based Diagnostic Models by Mining Textual Diagnostic Reports of SPECT Scans

Journal of Computer and Communications 2021年第5期9卷 10-19页

作者： Chuangui Cao Chengcheng Han Qiang Lin School of Mathematics and Computer Science Northwest Minzu University Lanzhou China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou China

Mining rich semantic information hidden in heterogeneous information network is one of the important tasks of data mining. Generally, a nuclear medicine text consists of the description of disease (i.e., lesions) and diagnostic results. However, how to construct a computer-aided diagnostic model with a large number of medical texts is a challenging task. To automatically diagnose diseases with SPECT imaging, in this work, we create a knowledge-based diagnostic model by exploring the association between a disease and its properties. Firstly, an overview of nuclear medicine and data mining is presented. Second, the method of preprocessing textual nuclear medicine diagnostic reports is proposed. Last, the created diagnostic modes based on random forest and SVM are proposed. Experimental evaluation conducted real-world data of diagnostic reports of SPECT imaging demonstrates that our diagnostic models are workable and effective to automatically identify diseases with textual diagnostic reports.

关键词： Text Classification Nuclear Medicine SPECT Imaging Diagnostic Model Random Forest SVM

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Collaborative filtering recommendation algorithm integrating user similarity and trust 4

Collaborative filtering recommendation algorithm integrating...

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2021 4th International Conference on Computer Information Science and application Technology, CISAT 2021

作者： Huang, Mengge Cao, Kai Zhang, Jingyi Zhang, Chuanlin Deng, Tao Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou730030 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou730030 China School of Mathematics and Computer Science Northwest Minzu University Lanzhou730030 China

The rapid development of the Internet has brought convenience to people and has also produced the problem of "information overload". In view of the traditional collaborative filtering algorithm facing some bottlenecks to be solved, this study proposes a collaborative filtering algorithm that combines similarity and trust. First of all, in view of the large deviation of traditional similarity calculation and prediction of user ratings, this study proposes an optimized Pearson correlation coefficient calculation method;secondly, the trust relationship is established based on the user's rating of the common project, and the trust relationship between users who do not have a direct trust relationship is established through the transfer of trust;then find the nearest neighbor set of the target user through the fusion of user similarity and trust;finally, the item is scored and predicted to generate a recommendation list. Experimental results show that the algorithm proposed in this study can effectively improve the accuracy of recommendation. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： Collaborative filtering

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Bone metastasis segmentation based on Improved U-NET algorithm 4

Bone metastasis segmentation based on Improved U-NET algorit...

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

作者： Zhang, Jingyi Huang, Mengge Deng, Tao Cao, Yongchun Lin, Qiang Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou730030 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou730030 China School of Mathematics and Computer Science Northwest Minzu University Lanzhou730030 China

Whole body bone scan image analysis is widely used in nuclear medicine to assist nuclear medicine physicians in the detection of bone metastases. At present, the analysis of whole-body bone scan images mainly relies on the manual reading of nuclear medicine doctors. The doctors, based on personal knowledge and experience, look for abnormal lesion locations and diagnose them by examining the whole-body bone scan images. However, this method is prone to misdiagnosis and missed diagnosis. To solve the above problems, this study proposes an image segmentation method based on deep learning, which can automatically identify the location of bone metastases, so that doctors can make more accurate diagnosis. The Methods Attention mechanism was added to the jump connection of the original U-NET network to enhance the image feature selection. Experiments show that the algorithm in this study teaches traditional U-Net to show better results on the three indicators of MIoU Dice and MAP. © Published under licence by IOP Publishing Ltd.

关键词： Diagnosis

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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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International Conference on Intelligent computing, Automation and Systems (ICICAS)

作者： Ruiting Gao Qiang Lin Mingyang Luo Zhengxing Man Yongchun Cao Haijun Wang 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 Lanzhou China

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.

关键词： Image segmentation Machine learning algorithms Bones Metastasis Lesions Single photon emission computed tomography Ribs

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Deep learning SPECT lung perfusion image classification method based on attention mechanism 5

Deep learning SPECT lung perfusion image classification meth...

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2020 5th International Seminar on Computer Technology, Mechanical and Electrical Engineering, ISCME 2020

作者： Zeng, Sitao Cao, Yongchun Lin, Qiang Man, Zhengxing Deng, Tao Wang, Rong Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou Gansu730030 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou Gansu730030 China Department of Nuclear Medicine Gansu Provincial Hospital Lanzhou Gansu730030 China

SPECT lung perfusion is an important functional imaging technology. It can capture the functional lesions of the lung in a non-invasive manner and has become an important clinical detection method for diseases such as pulmonary embolism. In order to realize the automatic detection of the degree of pulmonary embolism, this paper studies and constructs a deep classification model based on the attention mechanism. First, the normalization technique is used to convert the original lung perfusion file into a SPECT image;secondly, in view of the over-fitting phenomenon of the deep learning model caused by the small amount of medical image data and the unbalanced data, the image translation and rotation techniques are used to perform effective expansion;then, in order to improve the model's feature extraction ability, the attention mechanism is combined with the depth classification model to build a SPECT lung perfusion image classification model;finally, a set of real SPECT lung perfusion images were used to carry out comparative experiments on various depth classification models. The experimental results show that the model proposed in this paper can effectively detect the extent of lung disease lesions, and the classification accuracy rate exceeds 88%, which verifies the effectiveness and reliability of the classification model. © Published under licence by IOP Publishing Ltd.

关键词： Image classification

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SPECT bone imaging thyroid lesion segmentation

SPECT bone imaging thyroid lesion segmentation

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2021 International Conference on Big data and Intelligent Algorithms, BDIA 2021

作者： Mingyang-Luo Qiang-Lin Tongtong-Li Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Lanzhou730030 China School of Mathematics and Computer Science Northwest Minzu University Lanzhou730030 China Key Laboratory of Streaming Data Computing and Application Northwest Minzu University Lanzhou730124 China

Nuclear medicine SPECT is the main functional imaging modality, which plays an important role in the diagnosis and treatment of thyroid diseases. Hyperthyroidism is a common thyroid disease with symptoms such as exophthalmos, eyelid edema, and vision loss. In order to accurately segment thyroid lesions from SPECT thyroid data, a U-net-based thyroid lesion segmentation model was constructed. First, mirror, rotate, and translate the existing data. At the same time, use the generative adversarial network to expand the data. Then build a segmentation model based on U-net. Finally, perform experimental evaluation and analysis based on a set of real SPECT thyroid data. The results show that: in the traditional data expansion, the IoU value of the improved RS-U-net model is 66.83. © Published under licence by IOP Publishing Ltd.

关键词： Nuclear medicine

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A Fusion Neural Network Incorporating Attention for Sensor-Based Human Activity Recognition 3

A Fusion Neural Network Incorporating Attention for Sensor-B...

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3rd International Conference on Computer Vision, Image and Deep Learning and International Conference on Computer Engineering and applications, CVIDL and ICCEA 2022

作者： Lu, Limeng Zhang, Chuanlin Cao, Kai Deng, Dao Institute of Chinese Ethnic Information Technology Northwest Minzu University Lanzhou China Key Laboratory of China's Ethnic Languages and Information Technology Ministry of Education Lanzhou China Northwest Minzu University School of Mathematics and Computer Science Lanzhou China Northwest Minzu University Key Laboratory of Streaming Data Computing Technologies and Application Lanzhou China

ISBN: (纸本)9781665459112

Even though the RNN, LSTM, and other networks are used to extract dependencies in time series, sensor-based human behavior recognition (HAR) still faces some difficulties, and the ability of deep learning (DL) networks to extract features still needs to be improved. We propose a fusion neural network in which an optimized small optimized Convolutional Block Attention Module (MP-CBAM) is suitable for HAR tasks to extract samples. The MP-CBAM is added to two branches Convolutional Neural Network (CNN) with different convolution kernel sizes, and the fused features are labeled with GRU for temporal dependence. Then the softmax function is used for classification. We validate on benchmark datasets UCI-HAR and WISDM and accuracies of 97.15% and 9S.9S% are obtained, respectively, demonstrating the adaptation of our framework to both single-sensor and multi-sensor devices. © 2022 IEEE.

关键词： Convolution

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Classifying SPECT Bone Scan Images Using Improved Convolutional Neural Networks 2

Classifying SPECT Bone Scan Images Using Improved Convolutio...

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

作者： Wang, Yubo Lin, Qiang Cao, Xu Cao, Yongchun Man, Zhengxing Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Gansu Lanzhou730030 China School of Mathematics and Computer Science Northwest Minzu University Gansu Lanzhou730124 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Gansu Lanzhou730030 China

ISBN: (纸本)9781510657694

SPECT imaging is an effective medical method for diagnosis, treatment, evaluation, and prevention of a range of serious diseases and medical conditions. SPECT bone scans have the potential to provide more accurate assessment of disease staging and severity. To classify medical images more effectively, in this work, we propose an AlexNet-based image classification model. Specifically, the network structure of AlexNet is fine-tuned and attention mechanism is added to construct an improved model. Lastly, experimental results show that the improved AlexNet classification model is feasible for classifying SEPCT bone scan images, with obtaining scores of 0.7377, 0.7677, 0.7723, and 0.7674 for Acc, Pre, Rec and F-1, respectively. © 2022 SPIE.

关键词： Image classification

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On Star Coloring of Mycielski's Graphs for Special Graphs

On Star Coloring of Mycielski's Graphs for Special Graphs

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Computer Science and Management Technology (ICCSMT), International Conference on

作者： Cai Jin Shuangliang Tian Zhuomo An School of Mathematics and Computer Science Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Northwest Minzu University Lanzhou China

ISBN: (纸本)9781728186696

Let G be a simple graph with vertex set V (G) and edge set E (G). A vertex coloring of G is called a star coloring of G if any path of 4 order is bicolored. The minimum number of colors required for a star coloring of G is denoted by χ s (G). Let G is a simple graph with vertex set V (G) = V and edge set E (G) = E, if the vertex set is V ∪ V' ∪ {w} where V' = {X' : x ∈ V} and the edge set is EU{xy' : xy∈V} U {y'w : y'∈V'}, then G is a Mycielski's graph. This paper mainly studies star coloring of Mycielski's graph for some special graphs, the specific results are as follows: if n = 2,3 , then χ s (M (P n )) = 4, otherwise, χ s (M (P n )) = 6 ; if n = 5, then χ s (M(C n )) = 8, otherwise, χ s (M(C n )) = 6 ; χ s (M(S n )) = 4 ; χ s (M (K n )) = n + 2.

关键词： Computer science Color

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The acyclic coloring of the Corona product and Edge corona product of special graphs

The acyclic coloring of the Corona product and Edge corona p...

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Computer Science and Management Technology (ICCSMT), International Conference on

作者： Qiqi Lu Shuangliang Tian Lizhen Peng School of Mathematics and Computer Science Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Northwest Minzu University Lanzhou China

An acyclic coloring of a graph is a proper vertex coloring such that there are no bichromatic cycles. The acyclic chromatic number of G, denoted a(G), is the minimum number of colors required for acyclic coloring of a graph G. We study the acyclic coloring of the corona product, edge corona product of paths or circles and any simple graph, and the corresponding acyclic chromatic number are determined.

关键词： Computer science Color Corona

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