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750 results on '"Chen, Chaoyang"'

2. Automatic diagnosis of cardiac magnetic resonance images based on semi-supervised learning

Author

Huang, Hejun, Chen, Zuguo, Huang, Yi, Luo, Guangqiang, Chen, Chaoyang, and Song, Youzhi

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Cardiac magnetic resonance imaging (MRI) is a pivotal tool for assessing cardiac function. Precise segmentation of cardiac structures is imperative for accurate cardiac functional evaluation. This paper introduces a semi-supervised model for automatic segmentation of cardiac images and auxiliary diagnosis. By harnessing cardiac MRI images and necessitating only a small portion of annotated image data, the model achieves fully automated, high-precision segmentation of cardiac images, extraction of features, calculation of clinical indices, and prediction of diseases. The provided segmentation results, clinical indices, and prediction outcomes can aid physicians in diagnosis, thereby serving as auxiliary diagnostic tools. Experimental results showcase that this semi-supervised model for automatic segmentation of cardiac images and auxiliary diagnosis attains high accuracy in segmentation and correctness in prediction, demonstrating substantial practical guidance and application value.

Published
2024

7. Channel prior convolutional attention for medical image segmentation

Author

Huang, Hejun, Chen, Zuguo, Zou, Ying, Lu, Ming, and Chen, Chaoyang

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Characteristics such as low contrast and significant organ shape variations are often exhibited in medical images. The improvement of segmentation performance in medical imaging is limited by the generally insufficient adaptive capabilities of existing attention mechanisms. An efficient Channel Prior Convolutional Attention (CPCA) method is proposed in this paper, supporting the dynamic distribution of attention weights in both channel and spatial dimensions. Spatial relationships are effectively extracted while preserving the channel prior by employing a multi-scale depth-wise convolutional module. The ability to focus on informative channels and important regions is possessed by CPCA. A segmentation network called CPCANet for medical image segmentation is proposed based on CPCA. CPCANet is validated on two publicly available datasets. Improved segmentation performance is achieved by CPCANet while requiring fewer computational resources through comparisons with state-of-the-art algorithms. Our code is publicly available at \url{https://github.com/Cuthbert-Huang/CPCANet}.

Published
2023

9. Complementary consistency semi-supervised learning for 3D left atrial image segmentation

Author

Huang, Hejun, Chen, Zuguo, Chen, Chaoyang, Lu, Ming, and Zou, Ying

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

A network based on complementary consistency training, called CC-Net, has been proposed for semi-supervised left atrium image segmentation. CC-Net efficiently utilizes unlabeled data from the perspective of complementary information to address the problem of limited ability of existing semi-supervised segmentation algorithms to extract information from unlabeled data. The complementary symmetric structure of CC-Net includes a main model and two auxiliary models. The complementary model inter-perturbations between the main and auxiliary models force consistency to form complementary consistency. The complementary information obtained by the two auxiliary models helps the main model to effectively focus on ambiguous areas, while enforcing consistency between the models is advantageous in obtaining decision boundaries with low uncertainty. CC-Net has been validated on two public datasets. In the case of specific proportions of labeled data, compared with current advanced algorithms, CC-Net has the best semi-supervised segmentation performance. Our code is publicly available at https://github.com/Cuthbert-Huang/CC-Net.

Published
2022

16. Advances in the Adjunctive Treatment of Lennox-Gastaut Syndrome with Clobazam and Cannabidiol

Author

SHI Jingtian, CHEN Chaoyang, YANG Ting, WEI Ran, ZHANG Xuanling, WANG Zining, HU Xiaojuan, and ZHOU Ying

Subjects
rare diseases, lennox-gastaut syndrome, clobazam, cannabidiol, orplan drugs, Medicine
Abstract

Lennox-Gastaut syndrome (LGS) is a severe, epileptic encephalopathy.In recent years, a variety of drugs have been approved for the treatment of LGS. The U.S. Food and Drug Administration approved clobazam and cannabidiol as adjunctive therapy for LGS in October 2011 and June 2018, respectively. This article provides an overview of clobazam and cannabidiol, including their chemical structures, pharmacological actions, curative effects, safety profile, drug interactions, to introduce the current state of research and the achievements of both drugs.

Published
2024
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17. Progress in the Diagnosis and Treatment of Infantile Epileptic Spasm Syndrome

Author

ZHENG Lu, YANG Ting, CHEN Chaoyang, WEI Ran, ZHANG Xuanling, MA Yijie, and ZHOU Ying

Subjects
rare dieases, infantile epileptic spasm syndrome, antiepileptic, Medicine
Abstract

Infantile epileptic spasm syndrome (IESS) is a new concept proposed recently. IESS is a unique and age-specific refractory epilepsy syndrome. The recent advances in molecular biology, neuroimmunology and the in-depth study of anti-epileptic mechanism in antiepileptic drugs have led to the achievements in the definition and treatment of infantile epileptic spasm. At present, the use of traditional antiepileptic drugs is decreasing, while the use of new antiepileptic drugs is increasing. In this paper, based on the relevant literature in recent years, the authors discuss the pathogenesis, epidemiology, etiology, diagnosis, treatment, therapeutic drugs, clinical progress, efficacy, and safety of infantile epileptic spasm, hoping to introduce the latest status in research and achievements of IESS.

Published
2024
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18. Advances in Disease-Modifying Therapy of Neuromyelitis Optic Spectrum Disorders

Author

ZHANG Xuanling, CHEN Chaoyang, WEI Ran, and ZHOU Ying

Subjects
neuromyelitis optic spectrum disorders, disease-modifying therapy, medication, Medicine
Abstract

Neuromyelitis optic spectrum disorders (NMOSD) is a serious inflammatory demyelinating disease. Clinical treatment of NMOSD mainly involves acute phase treatment and remission phase prevention treatment. Disease-modifying therapy (DMT) in remission phase of NMOSD can effectively reduce disease recurrence and delay disability progression. At present, the drugs commonly used for DMT at home and abroad include classical immunosuppression, B-cell surface antigen monoclonal antibody, complement targeting monoclonal antibody, IL-6 receptor-targeting monoclonal antibody, etc. This paper mainly reviews the DMT drugs efficacy, safety, as well as the marketing status of NMOSD.

Published
2024
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46. Study on the Vulnerability of Power Grid Cascade Failures Based on Complex Network Theory

Author

Long, Yirong, Chen, Chaoyang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, 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, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, 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, Zhang, Junjie James, Series Editor, Jia, Yingmin, editor, Zhang, Weicun, editor, and Fu, Yongling, editor

Published
2021
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48. Long noncoding RNA NR2F1-AS1 stimulates the tumorigenic behavior of non-small cell lung cancer cells by sponging miR-363-3p to increase SOX4

Author

Jin Luming, Chen Chaoyang, Huang Lipeng, Sun Qingyu, and Bu Liang

Subjects
non-small cell lung cancer, glycolysis, nr2f1-as1, mir-363-3p, sox4, Medicine
Abstract

Long noncoding RNA (lncRNA), specifically the upregulation of lncRNA NR2F1 antisense RNA 1 (NR2F1-AS1), has been involved in the progression of non-small cell lung cancer (NSCLC), but the mechanisms that underlie this remain unclear. In this study, the expression of NR2F1-AS1, miR-363-3p, and SOX4 was assessed in NSCLC cells. A loss-of-function assay was used to measure the tumorigenicity of NSCLC cells. The glycolysis and glutamine metabolism of NSCLC cells was also measured via extracellular acidification rate, consumption of glucose and glutamine, and production of lactate and ATP. The relationships among NR2F1-AS1, miR-363-3p, and SOX4 were detected via dual-luciferase reporter assay. HK-2, GLS1, and SOX4 levels were also analyzed. We found that both NSCLC tissues and cells had higher levels of NR2F1-AS1. Silencing of NR2F1-AS1 inhibited the tumorigenicity of cells in vitro and reduced the glycolysis and glutamine metabolism of NSCLC cells. Regarding its mechanism, NR2F1-AS1 positively regulated the SOX4 level by sponging miR-363-3p. Furthermore, miR-363-3p inhibition or SOX4 overexpression reversed the repressing role of sh-NR2F1-AS1 in the tumorigenicity of NSCLC cells. In summary, NR2F1-AS1 promotes the tumorigenicity of NSCLC cells by regulating miR-363-3p/SOX4.

Published
2021
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