Meng Niu

TL;DR: A fully automatic deep learning system is proposed for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography that automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.

TL;DR: Based on deep learning method, the proposed diagnosis model trained on multi-view images of chest CT images showed great potential to improve the efficacy of diagnosis and mitigate the heavy workload of radiologists for the initial screening of COVID-19 pneumonia.

TL;DR: Deep learning provides a convenient tool for fast screening COVID-19 and finding potential high-risk patients, which may be helpful for medical resource optimization and early prevention before patients show severe symptoms.

TL;DR: De-COVID19-Net can non-invasively predict whether a patient will die shortly based on the patient's initial CT scan with an impressive performance, which indicated that it could be used as a potential prognosis tool to alert high-risk patients and intervene in advance.

TL;DR: The methodological process in liver disease radiomics studies is reviewed in a stepwise fashion from data acquisition and curation, region of interest segmentation, liver‐specific feature extraction, to task‐oriented modelling, and the applications of radiomics in liver diseases are outlined.