Multi-task vision transformer using low-level chest X-ray feature corpus for COVID-19 diagnosis and severity quantification

•Backbone with pre-built large CXR dataset is less prone to overfitting.•A multi-task Vision Transformer leveraging low-level features from the backbone was devised.•The proposed multi-task model was capable of performing both classification and severity prediction simultaneously.•The proposed model...

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Vydané v:	Medical image analysis Ročník 75; s. 102299
Hlavní autori:	Park, Sangjoon, Kim, Gwanghyun, Oh, Yujin, Seo, Joon Beom, Lee, Sang Min, Kim, Jin Hwan, Moon, Sungjun, Lim, Jae-Kwang, Ye, Jong Chul
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier B.V 01.01.2022 Elsevier BV
Predmet:	Algorithms Chest Chest X-ray Computer networks Coronavirus disease-19 Coronaviruses COVID-19 COVID-19 Testing Datasets Diagnosis Edema Embedding Feature extraction Humans Image processing Medical diagnosis Multi-task learning Opacity Pandemics SARS-CoV-2 Viral diseases Vision Vision transformer X-Rays Vision transformer Coronavirus disease-19 Chest X-ray 41A10 65D05 65D17 Multi-task learning 41A05
ISSN:	1361-8415, 1361-8423, 1361-8423
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