TY - GEN
T1 - COVID-19 Infection Percentage Prediction via Boosted Hierarchical Vision Transformer
AU - Hsu, Chih Chung
AU - Dai, Sheng Jay
AU - Chen, Shao Ning
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85136083656&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85136083656&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-13324-4_45
DO - 10.1007/978-3-031-13324-4_45
M3 - Conference contribution
AN - SCOPUS:85136083656
SN - 9783031133237
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 529
EP - 535
BT - Image Analysis and Processing. ICIAP 2022 Workshops - ICIAP International Workshops, Revised Selected Papers
A2 - Mazzeo, Pier Luigi
A2 - Distante, Cosimo
A2 - Frontoni, Emanuele
A2 - Sclaroff, Stan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st International Conference on Image Analysis and Processing , ICIAP 2022
Y2 - 23 May 2022 through 27 May 2022
ER -