Author: Xiang Bai; Cong Fang; Yu Zhou; Song Bai; Zaiyi Liu; Qianlan Chen; Yongchao Xu; Tian Xia; Shi Gong; Xudong Xie; Dejia Song; Ronghui Du; Chunhua Zhou; Chengyang Chen; Dianer Nie; Dandan Tu; Changzheng Zhang; Xiaowu Liu; Lixin Qin; Weiwei Chen
Title: Predicting COVID-19 malignant progression with AI techniques Document date: 2020_3_23
ID: 50oy9qqy_30
Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.20.20037325 doi: medRxiv preprint Our study has several limitations. First, samples available for malignant progression prediction were limited. The diverse data in the large scale dataset will allow deep learning-based methods to gain a more comprehensive understanding of what causes the malignant progression of mild patients. Second, th.....
Document: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.20.20037325 doi: medRxiv preprint Our study has several limitations. First, samples available for malignant progression prediction were limited. The diverse data in the large scale dataset will allow deep learning-based methods to gain a more comprehensive understanding of what causes the malignant progression of mild patients. Second, the quantitative information of CT data is not detailed enough. Using the richer original features included in pixel-wise segmentation results of the CT scans, the predictive model may perform better.
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