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17 results on '"Zheng, Tianlei"'

4. An imaging-based artificial intelligence model for non-invasive grading of hepatic venous pressure gradient in cirrhotic portal hypertension

Author

Yu, Qian, Huang, Yifei, Li, Xiaoguo, Pavlides, Michael, Liu, Dengxiang, Luo, Hongwu, Ding, Huiguo, An, Weimin, Liu, Fuquan, Zuo, Changzeng, Lu, Chunqiang, Tang, Tianyu, Wang, Yuancheng, Huang, Shan, Liu, Chuan, Zheng, Tianlei, Kang, Ning, Liu, Changchun, Wang, Jitao, Akçalar, Seray, Çelebioğlu, Emrecan, Üstüner, Evren, Bilgiç, Sadık, Fang, Qu, Fu, Chi-Cheng, Zhang, Ruiping, Wang, Chengyan, Wei, Jingwei, Tian, Jie, Örmeci, Necati, Ellik, Zeynep, Asiller, Özgün Ömer, Ju, Shenghong, and Qi, Xiaolong

Published
2022
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7. Corrigendum: Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: a multicenter study

Author

Wang, Jitao, primary, Zheng, Tianlei, additional, Liao, Yong, additional, Geng, Shi, additional, Li, Jinlong, additional, Zhang, Zhanguo, additional, Shang, Dong, additional, Liu, Chengyu, additional, Yu, Peng, additional, Huang, Yifei, additional, Liu, Chuan, additional, Liu, Yanna, additional, Liu, Shanghao, additional, Wang, Mingguang, additional, Liu, Dengxiang, additional, Miao, Hongrui, additional, Li, Shuang, additional, Zhang, Biao, additional, Huang, Anliang, additional, Zhang, Yewei, additional, Qi, Xiaolong, additional, and Chen, Shubo, additional

Published
2023
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9. Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study

Author

Wang, Jitao, primary, Zheng, Tianlei, additional, Liao, Yong, additional, Geng, Shi, additional, Li, Jinlong, additional, Zhang, Zhanguo, additional, Shang, Dong, additional, Liu, Chengyu, additional, Yu, Peng, additional, Huang, Yifei, additional, Liu, Chuan, additional, Liu, Yanna, additional, Liu, Shanghao, additional, Wang, Mingguang, additional, Liu, Dengxiang, additional, Miao, Hongrui, additional, Li, Shuang, additional, Zhang, Biao, additional, Huang, Anliang, additional, Zhang, Yewei, additional, Qi, Xiaolong, additional, and Chen, Shubo, additional

Published
2022
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16. The development trend and prospect of automobile energy-saving standard system under the goal of peak carbon dioxide emissions

Author

Ji Mengxue, Zheng Tianlei, Bao Xiang, Wang Zhao, Liu zhichao, and Liu Shaohui

Subjects
Environmental sciences, chemistry.chemical_compound, chemistry, business.industry, Carbon dioxide, Automotive industry, Fuel efficiency, Environmental science, GE1-350, business, Automotive engineering, Standard system
Abstract

This paper conducts research on the development trend of automobile energy-saving standard system under China’s goal of peak carbon dioxide emissions by 2030. The research first sorted out the carbon dioxide emission standards and regulations of major automobile developed countries in the world, systematically analyzed the current status of China's automobile energy-saving standard system, and proposed the key problems at this stage. With the goal of peak carbon dioxide emissions as the core, the key tasks for the next phase of the construction of the automotive energy-saving standard system are proposed, including comprehensively promoting the formulation of fuel consumption standards for passenger cars and commercial vehicles from 2025 to 2030, and accelerating the construction of NEV energy-saving standard system.

Published
2021

17. [An Improved Object Detection Algorithm for Thyroid Nodule Ultrasound Image Based on Faster R-CNN].

Author

Zheng T, Yang N, Geng S, Zhao X, Wang Y, Cheng D, and Zhao L

Subjects
Humans, Neural Networks, Computer, Algorithms, Ultrasonography methods, Thyroid Nodule diagnostic imaging
Abstract

Objective: To propose an improved algorithm for thyroid nodule object detection based on Faster R-CNN so as to improve the detection precision of thyroid nodules in ultrasound images., Methods: The algorithm used ResNeSt50 combined with deformable convolution (DC) as the backbone network to improve the detection effect of irregularly shaped nodules. Feature pyramid networks (FPN) and Region of Interest (RoI) Align were introduced in the back of the trunk network. The former was used to reduce missed or mistaken detection of thyroid nodules, and the latter was used to improve the detection precision of small nodules. To improve the generalization ability of the model, parameters were updated during backpropagation with an optimizer improved by Sharpness-Aware Minimization (SAM)., Results: In this experiment, 6 261 thyroid ultrasound images from the Affiliated Hospital of Xuzhou Medical University and the First Hospital of Nanjing were used to compare and evaluate the effectiveness of the improved algorithm. According to the findings, the algorithm showed optimization effect to a certain degree, with the AP50 of the final test set being as high as 97.4% and AP@50:5:95 also showing a 10.0% improvement compared with the original model. Compared with both the original model and the existing models, the improved algorithm had higher detection precision and improved capacity to detect thyroid nodules with better accuracy and precision. In particular, the improved algorithm had a higher recall rate under the requirement of lower detection frame precision., Conclusion: The improved method proposed in the study is an effective object detection algorithm for thyroid nodules and can be used to detect thyroid nodules with accuracy and precision., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).)

Published
2023
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