TY - GEN
T1 - Adaptive Distribution Learning with Statistical Hypothesis Testing for COVID-19 CT Scan Classification
AU - Chen, Guan Lin
AU - Hsu, Chih Chung
AU - Wu, Mei Hsuan
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85123051721&partnerID=8YFLogxK
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U2 - 10.1109/ICCVW54120.2021.00057
DO - 10.1109/ICCVW54120.2021.00057
M3 - Conference contribution
AN - SCOPUS:85123051721
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 471
EP - 479
BT - Proceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
Y2 - 11 October 2021 through 17 October 2021
ER -