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205 results on '"Li, Liujun"'

41. An integration–competition network for bridge crack segmentation under complex scenes.

Author

Sun, Lixiang, Yang, Yixin, Zhou, Guoxiong, Chen, Aibin, Zhang, Yukai, Cai, Weiwei, and Li, Liujun

Abstract

The segmentation accuracy of bridge crack images is influenced by high‐frequency light, complex scenes, and tiny cracks. Therefore, an integration–competition network (complex crack segmentation network [CCSNet]) is proposed to address these problems. First, a grayscale‐oriented adjustment algorithm is proposed to solve the high‐frequency light problem. Second, an integration–competition mechanism is proposed to detach complex backgrounds and grayscale features of cracks. Finally, a tiny attention mechanism is proposed to extract the shallow features of tiny cracks. CCSNet outperforms seven state‐of‐the‐art crack segmentation methods in both generalization and comparison experiments on self‐built dataset and four public datasets. It also achieved excellent performance in practical bridge crack tests. Therefore, CCSNet is an effective auxiliary method for lowering the cost of bridge safety detection. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease.

Author

Chen, Ziman, Ying, Tin Cheung, Chen, Jiaxin, Wu, Chaoqun, Li, Liujun, Chen, Hui, Xiao, Ting, Huang, Yongquan, Chen, Xuehua, Jiang, Jun, Wang, Yingli, Lu, Wuzhu, and Su, Zhongzhen

Subjects
RENAL fibrosis, CHRONIC kidney failure, RECEIVER operating characteristic curves, MACHINE learning, KIDNEY cortex, KIDNEY diseases
Abstract

Given its progressive deterioration in the clinical course, noninvasive assessment and risk stratification for the severity of renal fibrosis in chronic kidney disease (CKD) are required. We aimed to develop and validate an end-to-end multilayer perceptron (MLP) model for assessing renal fibrosis in CKD patients based on real-time two-dimensional shear wave elastography (2D-SWE) and clinical variables. From April 2019 to December 2021, a total of 162 patients with CKD who underwent a kidney biopsy and 2D-SWE examination were included in this single-center, cross-sectional, and prospective clinical study. 2D-SWE was performed to measure the right renal cortex stiffness, and the corresponding elastic values were recorded. Patients were categorized into two groups according to their histopathological results: mild and moderate-severe renal fibrosis. The patients were randomly divided into a training cohort (n = 114) or a test cohort (n = 48). The MLP classifier using a machine learning algorithm was used to construct a diagnostic model incorporating elastic values with clinical features. Discrimination, calibration, and clinical utility were used to appraise the performance of the established MLP model in the training and test sets, respectively. The developed MLP model demonstrated good calibration and discrimination in both the training [area under the receiver operating characteristic curve (AUC) = 0.93; 95% confidence interval (CI) = 0.88 to 0.98] and test cohorts [AUC = 0.86; 95% CI = 0.75 to 0.97]. A decision curve analysis and a clinical impact curve also showed that the MLP model had a positive clinical impact and relatively few negative effects. The proposed MLP model exhibited the satisfactory performance in identifying the individualized risk of moderate-severe renal fibrosis in patients with CKD, which is potentially helpful for clinical management and treatment decision-making. [ABSTRACT FROM AUTHOR]

Published
2023
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