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

Guan Lin Chen, Chih Chung Hsu, Mei Hsuan Wu

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題Proceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
發行者Institute of Electrical and Electronics Engineers Inc.
頁面471-479
頁數9
ISBN(電子)9781665401913
DOIs
出版狀態Published - 2021
事件18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 - Virtual, Online, Canada
持續時間: 2021 10月 112021 10月 17

出版系列

名字Proceedings of the IEEE International Conference on Computer Vision
2021-October
ISSN(列印)1550-5499

Conference

Conference18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
國家/地區Canada
城市Virtual, Online
期間21-10-1121-10-17

All Science Journal Classification (ASJC) codes

  • 軟體
  • 電腦視覺和模式識別

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