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Your search keyword '"Xie, Wenqian"' showing total 129 results
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129 results on '"Xie, Wenqian"'

1. Multi-agent Graph Reinforcement Learning Based Cross-Layer Routing for Mobile Ad-Hoc Network

Author

Wang, Yuhao, Xie, Wenqian, Ding, Zhihan, Yang, Qianze, Lin, Yan, Zhang, Yijin, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Dong, Jian, editor, Zhang, Long, editor, and Cheng, Deqiang, editor

Published
2024
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10. Image Recognition Method of Cervical Adenocarcinoma in Situ Based on Deep Learning

Author

LIU Chang, ZHENG Yuchao, XIE Wenqian, LI Chen, and LI Xiaohan

Subjects
deep learning, cervical adenocarcinoma in situ, transfer learning, pathological diagnosis, Medicine
Abstract

Objective To construct a pathological image diagnostic model of cervical adenocarcinoma in situ(CAIS) based on deep learning algorithm. Methods Pathological tissue sections of CAIS and normal cervical canal and gland sections of chronic cervicitis stored in the Pathology Department of Shengjing Hospital, China Medical University from January 2019 to December 2021 were retrospectively collected. After image collection, they were randomly divided into training set, validation set and test set with a ratio of 4∶3∶3. The data of training set and validation set were used to conduct transfer learning training and parameter debugging for 6 network models, including VGG16, VGG19, Inception V3, Xception, ResNet50 and DenseNet201, and the convolutional neural network binary classification model that could recognize pathological images of CAIS was constructed. The models were combined to build the ensemble learning model. Based on the test set data, the performance of pathological image recognition of single model and ensemble learning model was evaluated. The results were expressed by operation time, accuracy, precision, recall, F1 score and area under the curve(AUC) of receiver operating characteristic. Results A total of 104 pathological sections of CAIS and 90 pathological sections of normal cervical duct and gland with chronic cervicitis were selected. A total of 500 pathological images of CAIS and normal cervical duct glands were collected, including 400 images of training set, 300 images of validation set and 300 images of test set, respectively. Among the 6 models, ResNet50 model, with the highest accuracy(87.33%), precision(90.00%), F1 score(86.90%) and AUC(0.87), second highest recall(84.00%) and shorter operation time(2062.04 s), demonstrated the best overall performance; VGG19 model was the second; and Inception V3 and Xception model had the worst performance.Among the 6 kinds of ensemble learning models, ResNet50 and DenseNet201 showed the best overall performance, and their accuracy, precision, recall, F1 score and AUC were 89.67%, 84.67%, 94.07%, 89.12% and 0.90, respectively. VGG19 and ResNet50 ensemble model followed. Conclusions It is feasible to construct CAIS pathological image recognition models by deep learning algorithm, among which ResNet50 models has higher overall performance. Ensemble learning can improve the recognition effect on pathological images by single model.

Published
2023
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24. Prognostic Significance of a Model Based on Acetaldehyde Dehydrogenase 2 Genetic Polymorphisms in Laryngeal Carcinoma.

Author

Ye, Yuchu, Wu, Taowei, Liang, Faya, Fan, Jianming, Song, Pan, Li, Yixin, Xie, Wenqian, Huang, Xiaoming, and Han, Ping

Abstract

Objective: Because of the high costs associated with early‐stage laryngeal carcinoma diagnosis and prognosis prediction, this study attempts to find valuable targets to establish a novel predictive model by focusing on the aldehyde dehydrogenase 2 (ALDH2) genotype and other peripheral blood markers. Study Design: Retrospective study. Setting: Tertiary comprehensive hospital. Methods: From January 2011 to January 2021, 362 cases of laryngeal carcinoma were included and divided into 2 groups in this retrospective analysis. Information on medical history, alcohol, and tobacco consumption habits, ALDH2 genotypes, and other peripheral blood markers was collected. Endpoints of the current study included disease‐free survival and overall survival. A nomogram model for overall survival was established and evaluated using receiver operating characteristic (ROC) curves. Results: A total of 236 patients were included in the training cohort, and the other 126 were included in the validation cohort. The median follow‐up of the patients was 9.6 years (interquartile range: 7.5‐12.5 years). Peripheral fibrinogen, hemoglobin, and ALDH2 genotypes were significantly associated with an increase in laryngeal carcinoma mortality rate on Kaplan‐Meier curves. The ROC curve showed that the effectiveness of overall survival prediction by the nomogram model was better than that of traditional clinical staging. Conclusion: A prognostic nomogram of laryngeal carcinoma patients involving ALDH2 and peripheral blood markers and T and N stages was constructed and validated. [ABSTRACT FROM AUTHOR]

Published
2023
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32. Research on Stimulating the County-wide Economic Vitality of Jingzhou City

Author

XIE Wenqian

Subjects
County-wide economy, Path selection, Vitality, Jingzhou City, Agribusiness
Abstract

Based on the actual situation of Jingzhou City in Hubei Province, this article deeply explores the advantages in stimulating the vitality of the county-wide economy, the opportunities in development, the shortcomings, and the selection of development paths, aiming to further promote the county-wide economy of Jingzhou City. The transition from unbalanced and inadequate development to high-quality development has promoted the overall vitality and leapfrog development of the county-wide economy of Jingzhou City.

Published
2021
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42. Synthesis of N‐Aryl‐ and N‐Alkenylhydrazides through C(sp2)−N Bond Construction.

Author

Lei, Jian, Xie, Wenqian, Li, Jing, Wu, Ya, and Xie, Xiaolan

Subjects
*BOND prices, *BONDS (Finance), *MOIETIES (Chemistry), *AZO compounds, *CARBON-carbon bonds
Abstract

N‐Aryl‐ and N‐alkenylhydrazides, which include a C(sp2)‐N−N motif, have attracted considerable attention not only in biologically active molecules but also because of their extensive technical applications in organic synthesis. C(sp2)−N bond construction protocols using an N‐acylhydrazine or azocarboxylic ester as the hydrazide source provide a powerful route to these compounds. These protocols exhibit different selectivity manner for the C(sp2) moiety and the regioselectivity issue of the N−N bond. This Minireview documents the available methods that have been developed based on different starting materials. The features and drawbacks of each subdivisional methodology are demonstrated. [ABSTRACT FROM AUTHOR]

Published
2021
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