Yutian Chong

TL;DR: Wang et al. as mentioned in this paper developed a deep learning-based CT diagnosis system to identify patients with COVID-19, which achieved an AUC of 0.99, recall (sensitivity) of 0.,93, and precision of 0,96.

TL;DR: A deep learning-based CT diagnosis system (DeepPneumonia) was developed and showed that the established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.

TL;DR: This study represented 3D structures by 2D maps of pairwise residue distances and developed a new method (SPROF) to predict protein sequence profile based on an image captioning learning frame, which is the first method to employ 2D distance map for predicting protein properties.

TL;DR: Zheng et al. as discussed by the authors proposed a new model by combining the ResNet50 backbone with SE blocks that was recently developed for fine image analysis, achieving an overall accuracy of 0.94.

TL;DR: This study offers a conceptual framework to understand the immune landscape of HBV-ALF, which might help to improve prognosis and suggest that hub genes were mainly related to immune response and apoptosis.