Beibei Jiang
Shanghai Jiao Tong University
8 Papers
15 Citations
Beibei Jiang is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 2, co-authored 2 publications.
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Papers
Simultaneous Identification of EGFR, KRAS, ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics
Tiening Zhang,Zhihan Xu,Guixue Liu,Beibei Jiang,Geertruida H. de Bock,Harry J.M. Groen,Rozemarijn Vliegenthart,Xueqian Xie +7 more
TL;DR: In this paper, a machine learning-derived radiomics approach was developed to simultaneously discriminate epidermal growth factor receptor (EGFR), Kirsten rat sarcoma viral oncogene (KRAS), Erb-B2 receptor tyrosine kinase 2 (ERBB2), and tumor protein 53 (TP53) genetic mutations in patients with non-small cell lung cancer (NSCLC).
29
Development and application of artificial intelligence in cardiac imaging.
Beibei Jiang,Ning Guo,Yinghui Ge,Lu Zhang,Matthijs Oudkerk,Xueqian Xie +5 more
TL;DR: It can be concluded that AI is widely applied in cardiac applications in the clinic, including coronary calcium scoring, coronary CT angiography, fractional flow reserve CT, plaque analysis, left ventricular myocardium analysis, diagnosis of myocardial infarction, prognosis of coronary artery disease, assessment of cardiac function, and diagnosis and prediction of cardiomyopathy.
21
Measurement Accuracy and Repeatability of RECIST-Defined Pulmonary Lesions and Lymph Nodes in Ultra-Low-Dose CT Based on Deep Learning Image Reconstruction
Keke Zhao,Beibei Jiang,Shuai Zhang,Lu Zhang,Lixin Zhang,Yan Feng,Jianying Li,Yaping Zhang,Xueqian Xie +8 more
TL;DR: It is found that the measured diameters in ULDCT were highly correlated with that of contrast-enhanced CT and highly repeatable, which may facilitate evaluating target lesions with greatly reduced radiation exposure in tumor evaluation and lung cancer screening.
6
Machine-learning-based radiomics identifies atrial fibrillation on the epicardial fat in contrast-enhanced and non-enhanced chest CT.
Lu Zhang,Zhihan Xu,Beibei Jiang,Yaping Zhang,Lingyun Wang,Geertruida H deBock,Rozemarijn Vliegenthart,Xueqian Xie +7 more
TL;DR: EAT-score generated by machine-learning-based radiomics achieved high performance in identifying patients with atrial fibrillation by analyzing epicardial adipose tissue (EAT) in CT images.
COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images.
TL;DR: CNN can identify emphysema and airway wall remodeling on CT images to infer lung function and determine the existence and severity of COPD, and provides an alternative way to detect COPD using the extensively available chest CT.