Jun Wang

TL;DR: This review paper covers the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up, and particularly focuses on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals.

TL;DR: A deep learning (DL) based segmentation system is developed to automatically quantify infection regions of interest (ROIs) and their volumetric ratios w.r.t. the lung and possible applications, including but not limited to analysis of follow-up CT scans and infection distributions in the lobes and segments correlated with clinical findings were discussed.

TL;DR: In this article, the authors reviewed the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19, including image acquisition, segmentation, diagnosis, and follow-up.

TL;DR: Extensive experimental results indicate that the proposed WV-Co-FCM algorithm outperforms or is at least comparable to the existing state-of-the-art multitask and multiview clustering algorithms and the importance of different views of the datasets can be effectively identified.

TL;DR: Transductive transfer learning is used to reduce the discrepancy in data distribution between the training and testing data, semi-supervised learning is employed to use the unlabeled testing data to remedy the shortage of training data, and TSK fuzzy system is adopted to increase model interpretability.