Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images

no code implementations • 26 Mar 2020 • Zhenyu Tang, Wei Zhao, Xingzhi Xie, Zheng Zhong, Feng Shi, Jun Liu, Dinggang Shen

Purpose: Using machine learning method to realize automatic severity assessment (non-severe or severe) of COVID-19 based on chest CT images, and to explore the severity-related features from the resulting assessment model.

Computed Tomography (CT)

Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images

no code implementations • 8 May 2020 • Kelei He, Wei Zhao, Xingzhi Xie, Wen Ji, Mingxia Liu, Zhenyu Tang, Feng Shi, Yang Gao, Jun Liu, Junfeng Zhang, Dinggang Shen

Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice).

Segmentation

A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning

no code implementations • 7 Feb 2021 • Zekun Li, Wei Zhao, Feng Shi, Lei Qi, Xingzhi Xie, Ying WEI, Zhongxiang Ding, Yang Gao, Shangjie Wu, Jun Liu, Yinghuan Shi, Dinggang Shen

How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world.

COVID-19 Diagnosis Data Augmentation +3