| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2024-10-17 |
| タイトル |
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タイトル |
Classification and visual explanation for COVID-19 pneumonia from CT images using triple learning |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Kato, Sota
Oda, Masahiro
Mori, Kensaku
Shimizu, Akinobu
大竹, 義人
Hashimoto, Masahiro
Akashi, Toshiaki
Hotta, Kazuhiro
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
This study presents a novel framework for classifying and visualizing pneumonia induced by COVID-19 from CT images. Although many image classification methods using deep learning have been proposed, in the case of medical image fields, standard classification methods are unable to be used in some cases because the medical images that belong to the same category vary depending on the progression of the symptoms and the size of the inflamed area. In addition, it is essential that the models used be transparent and explainable, allowing health care providers to trust the models and avoid mistakes. In this study, we propose a classification method using contrastive learning and an attention mechanism. Contrastive learning is able to close the distance for images of the same category and generate a better feature space for classification. An attention mechanism is able to emphasize an important area in the image and visualize the location related to classification. Through experiments conducted on two-types of classification using a three-fold cross validation, we confirmed that the classification accuracy was significantly improved; in addition, a detailed visual explanation was achieved comparison with conventional methods. |
| 書誌情報 |
en : Scientific Reports
巻 12,
号 1,
発行日 2022-12-02
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| 出版者 |
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出版者 |
Nature Research |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2045-2322 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1038/s41598-022-24936-6 |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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関連識別子 |
https://www.nature.com/articles/s41598-022-24936-6 |
| 権利 |
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権利情報Resource |
http://creativecommons.org/licenses/by/4.0/ |
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権利情報 |
$00A9 The Author(s) 2022 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| 著者版フラグ |
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出版タイプ |
NA |
