Ziwang Huang

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: SeTranSurv as discussed by the authors extracts patch features from WSIs through self-supervised learning and adaptively aggregates these features according to their spatial information and correlation between patches using the Transformer.

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: In this article, a coarse-refine segmentation network is proposed to segment the COVID-19 patients in CT images, and the atrous spatial pyramid pooling module is added to improve the performance in detecting infected regions with different scales.