Chen Xia

TL;DR: In this study, most infections among health care workers occurred during the early stage of the COVID-19 outbreak and in low-contagion areas; routine screening may be helpful in identifying asymptomatic carriers.

TL;DR: A novel multimodal 3D DenseNet model, with the properties of automatic feature extraction, and effective and high generalizability, M3D-DenseNet can serve as a useful method for other multi-modality radiogenomics problems and has the potential to be applied in clinical decision making.

TL;DR: DL shows promise to enhance the identification of pulmonary nodules and benefit nodule management and was assessed whether the radiologists' detection performance could be enhanced when using the DL model for assistance.

TL;DR: A deep learning algorithm for triaging patients with suspected COVID-19 at fever clinics was developed and externally validated for high accuracy and integration of this system into the standard clinical workflow could expedite identification of chest CT scans with imaging indications of CO VID-19.

TL;DR: In this paper, a Dense U-Net framework was used for the segmentation and quantification of intracerebral hemorrhage (ICH) and EDH, and SDH.