Coronavirus Detection and Analysis on Chest CT with Deep Learning
This work addresses the challenge of supporting radiologists in diagnosing COVID-19 during the pandemic, but it is incremental as it applies existing deep learning methods to a new medical dataset.
The researchers tackled the problem of detecting and analyzing COVID-19 from chest CT scans by developing a deep learning algorithm that detects, localizes, and quantifies disease severity, achieving results on a dataset of 110 confirmed patients.
The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed cases of which 80,881 are in "Mainland China" with 7,949 deaths, a mortality rate of 3.4%. In order to support radiologists in this overwhelming challenge, we develop a deep learning based algorithm that can detect, localize and quantify severity of COVID-19 manifestation from chest CT scans. The algorithm is comprised of a pipeline of image processing algorithms which includes lung segmentation, 2D slice classification and fine grain localization. In order to further understand the manifestations of the disease, we perform unsupervised clustering of abnormal slices. We present our results on a dataset comprised of 110 confirmed COVID-19 patients from Zhejiang province, China.