Adaptive Distribution Learning with Statistical Hypothesis Testing for COVID-19 CT Scan Classification

Guan Lin Chen, Chih Chung Hsu, Mei Hsuan Wu

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

Abstract

With the massive damage in the world caused by Coronavirus Disease 2019 SARS-CoV-2 (COVID-19), many related research topics have been proposed in the past two years. The Chest Computed Tomography (CT) scan is the most valuable materials to diagnose the COVID-19 symptoms. However, most schemes for COVID-19 classification of Chest CT scan are based on single slice-level schemes, implying that the most critical CT slice should be selected from the original CT volume manually. In this paper, a statistical hypothesis test is adopted to the deep neural network to learn the implicit representation of CT slices. Specifically, we propose an Adaptive Distribution Learning with Statistical hypothesis Testing (ADLeaST) for COVID-19 CT scan classification can be used to judge the importance of each slice in CT scan and followed by adopting the non-parametric statistics method, Wilcoxon signed-rank test, to make predicted result explainable and stable. In this way, the impact of out-of-distribution (OOD) samples can be significantly reduced. Meanwhile, a self-attention mechanism without statistical analysis is also introduced into the back-bone network to learn the importance of the slices explicitly. The extensive experiments show that both the proposed schemes are stable and superior. Our experiments also demonstrated that the proposed ADLeaST significantly outperforms the state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages471-479
Number of pages9
ISBN (Electronic)9781665401913
DOIs
Publication statusPublished - 2021
Event18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 - Virtual, Online, Canada
Duration: 2021 Oct 112021 Oct 17

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2021-October
ISSN (Print)1550-5499

Conference

Conference18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
Country/TerritoryCanada
CityVirtual, Online
Period21-10-1121-10-17

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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