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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

17.10.2022 | Original Article

Attention induction for a CT volume classification of COVID-19

verfasst von: Yusuke Takateyama, Takahito Haruishi, Masahiro Hashimoto, Yoshito Otake, Toshiaki Akashi, Akinobu Shimizu

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 2/2023

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Abstract

Purpose

This study proposes a method to draw attention toward the specific radiological findings of coronavirus disease 2019 (COVID-19) in CT images, such as bilaterality of ground glass opacity (GGO) and/or consolidation, in order to improve the classification accuracy of input CT images.

Methods

We propose an induction mask that combines a similarity and a bilateral mask. A similarity mask guides attention to regions with similar appearances, and a bilateral mask induces attention to the opposite side of the lung to capture bilaterally distributed lesions. An induction mask for pleural effusion is also proposed in this study. ResNet18 with nonlocal blocks was trained by minimizing the loss function defined by the induction mask.

Results

The four-class classification accuracy of the CT images of 1504 cases was 0.6443, where class 1 was the typical appearance of COVID-19 pneumonia, class 2 was the indeterminate appearance of COVID-19 pneumonia, class 3 was the atypical appearance of COVID-19 pneumonia, and class 4 was negative for pneumonia. The four classes were divided into two subgroups. The accuracy of COVID-19 and pneumonia classifications was evaluated, which were 0.8205 and 0.8604, respectively. The accuracy of the four-class and COVID-19 classifications improved when attention was paid to pleural effusion.

Conclusion

The proposed attention induction method was effective for the classification of CT images of COVID-19 patients. Improvement of the classification accuracy of class 3 by focusing on features specific to the class remains a topic for future work.

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Titel: Attention induction for a CT volume classification of COVID-19
verfasst von: Yusuke Takateyama
Takahito Haruishi
Masahiro Hashimoto
Yoshito Otake
Toshiaki Akashi
Akinobu Shimizu
Publikationsdatum: 17.10.2022
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 2/2023
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-022-02769-y

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Abstract

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