Beibei Jiang
Shanghai Jiao Tong University
6 Papers
2 Citations
Beibei Jiang is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Convolutional neural network & Deep learning. The author has an hindex of 2, co-authored 6 publications.
Chat about Author
Papers
Development and application of artificial intelligence in cardiac imaging.
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.
62
Development and multicenter validation of chest X-ray radiography interpretations based on natural language processing
Yaping Zhang,Yaping Zhang,Liu Mingqian,Shundong Hu,Yao Shen,Jun Lan,Beibei Jiang,Geertruida H. de Bock,Rozemarijn Vliegenthart,Chen Xu,Xueqian Xie,Xueqian Xie +11 more
- 28 Oct 2021
TL;DR: Zhang et al. as discussed by the authors developed and tested a 25-label classification system for abnormal findings described in the reports and validate their model using data from multiple sites, and show that their models achieve similar performance to interpretation from the radiologists themselves.
Classification of moving coronary calcified plaques based on motion artifacts using convolutional neural networks: a robotic simulating study on influential factors
Magdalena Dobrolinska,Magdalena Dobrolinska,Niels R van der Werf,Niels R van der Werf,Marcel J. W. Greuter,Marcel J. W. Greuter,Beibei Jiang,Riemer H. J. A. Slart,Riemer H. J. A. Slart,Xueqian Xie +9 more
TL;DR: In this article, the authors used convolutional neural networks (CNNs) to classify the motion-contaminated images of moving coronary calcified plaques and to determine the influential factors for the classification performance.
Human-recognizable CT image features of subsolid lung nodules associated with diagnosis and classification by convolutional neural networks.
Beibei Jiang,Yaping Zhang,Lu Zhang,Geertruida H. de Bock,Rozemarijn Vliegenthart,Xueqian Xie +5 more
TL;DR: In this article, a 5-fold cross-validation method for classifying subsolid nodules (SSNs) into three categories (benign and pre-invasive lesions [PL], minimally invasive adenocarcinoma [MIA], and invasive adnocarcinoma [IA]) was proposed.
Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review.
TL;DR: In this paper, a systematic review summarizes the latest progress of image segmentation, quantification, and the clinical application of AI in evaluating cardiac adipose tissue (EAT) using the QUADAS-2 tool.