COVID-19 Infection Percentage Prediction via Boosted Hierarchical Vision Transformer
21st International Conference on Image Analysis and Processing , ICIAP 2022
; 13374 LNCS:529-535, 2022.
Article
in English
| Scopus | ID: covidwho-2013966
ABSTRACT
A better backbone network usually benefits the performance of various computer vision applications. This paper aims to introduce an effective solution for infection percentage estimation of COVID-19 for the computed tomography (CT) scans. We first adopt the state-of-the-art backbone, Hierarchical Visual Transformer, as the backbone to extract the effective and semantic feature representation from the CT scans. Then, the non-linear classification and the regression heads are proposed to estimate the infection scores of COVID-19 symptoms of CT scans with the GELU activation function. We claim that multi-tasking learning is beneficial for better feature representation learning for the infection score prediction. Moreover, the maximum-rectangle cropping strategy is also proposed to obtain the region of interest (ROI) to boost the effectiveness of the infection percentage estimation of COVID-19. The experiments demonstrated that the proposed method is effective and efficient. © 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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