Deep Regression by Feature Regularization for COVID-19 Severity Prediction
21st International Conference on Image Analysis and Processing , ICIAP 2022
; 13374 LNCS:496-507, 2022.
Article
in English
| Scopus | ID: covidwho-2013963
ABSTRACT
During the COVID-19 worldwide pandemic, CT scan emerged as one of the most precise tool for identification and diagnosis of affected patients. With the increase of available medical imaging, Artificial Intelligence powered methods arisen to aid the detection and classification of COVID-19 cases. In this work, we propose a methodology to automatically inspect CT scan slices assessing the related disease severity. We competed in the ICIAP2021 COVID-19 infection percentage estimation competition, and our method scored in the top-5 at both the Validation phase ranking, with MAE = 4.912%, and Testing phase ranking, with MAE = 5.020%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
21st International Conference on Image Analysis and Processing , ICIAP 2022
Year:
2022
Document Type:
Article
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