COVID-19 Infection Percentage Prediction via Boosted Hierarchical Vision Transformer

Chih Chung Hsu, Sheng Jay Dai, Shao Ning Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationImage Analysis and Processing. ICIAP 2022 Workshops - ICIAP International Workshops, Revised Selected Papers
EditorsPier Luigi Mazzeo, Cosimo Distante, Emanuele Frontoni, Stan Sclaroff
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages7
ISBN (Print)9783031133237
Publication statusPublished - 2022
Event21st International Conference on Image Analysis and Processing , ICIAP 2022 - Lecce, Italy
Duration: 2022 May 232022 May 27

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13374 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st International Conference on Image Analysis and Processing , ICIAP 2022

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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