Zhong Xue

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: The main characteristic of the proposed methodology is that, by applying multivariate pattern classification methods, it can detect subtle and spatially complex patterns of morphological group differences which are often not detectable by voxel-based morphometric methods, because these methods analyze morphological measurements voxels-by-voxel and do not consider the entirety of the data simultaneously.

TL;DR: It is indicated that typical imaging characteristics and their changes can play crucial roles in the detection and management of COVID-19 and AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of the disease.

TL;DR: Enable technologies and systems suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals are reviewed.

TL;DR: In this paper, a deblocking filter is used to remove block noise from every block in a compressed video signal, but removing block noise requires a significant load on the deblocking filters.