Yongjian Zhu

Oct 29, 2024 | mdpi.com | Wenbin Li |Yongjian Zhu |Yafei Luo |Mingxing Wei

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Apr 9, 2024 | mdpi.com | Yafei Luo |Yongjian Zhu |Fei Huang

All articles published by MDPI are made immediately available worldwide under an open access license. No specialpermission is required to reuse all or part of the article published by MDPI, including figures and tables. Forarticles published under an open access Creative Common CC BY license, any part of the article may be reused withoutpermission provided that the original article is clearly cited. For more information, please refer tohttps://www.mdpi.com/openaccess.

Mar 11, 2024 | mdpi.com | Jiayu Wang |Ye Liu |Yongjian Zhu |Dong Wang

1. IntroductionIn the context of autonomous driving, the detection, recognition, and localization of key targets have always been one of the most critical tasks. With the development of sensor devices, computing facilities, and deep learning techniques, various algorithms for target detection have been continuously updated and iterated, leading to significant improvements in real-time performance and accuracy.

May 18, 2023 | academicradiology.org | Yongjian Zhu |Bing Feng |Wei Cai

ROC analysis results using the above significant parameters are presented in Table 3. AUCs of the 6 significant parameters ranged from 0.683 to 0.792 for differentiating MP from MN in HCC. Among these single parameters, Zeff-A showed the highest predictive effectiveness for discriminating MVI status, with an AUC of 0.792 (95% CI, 0.698-0.886), accuracy of 71.15%, sensitivity of 82.14%, specificity of 67.11%, PPV of 47.92%, and NPV of 91.07%.