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Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT
http://hdl.handle.net/10228/00009067
http://hdl.handle.net/10228/000090676834c9a2-f9b5-4f3d-abae-42c2e96bbc0d
名前 / ファイル | ライセンス | アクション |
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Item type | 学術雑誌論文 = Journal Article(1) | |||||
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公開日 | 2023-02-02 | |||||
タイトル | ||||||
タイトル | Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者 |
Uemura, Tomoki
× Uemura, Tomoki× Näppi, Janne J.× Watari, Chinatsu× Hironaka, Toru× Kamiya, Tohru× Yoshida, Hiroyuki |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Because of the rapid spread and wide range of the clinical manifestations of the coronavirus disease 2019 (COVID-19), fast and accurate estimation of the disease progression and mortality is vital for the management of the patients. Currently available image-based prognostic predictors for patients with COVID-19 are largely limited to semi-automated schemes with manually designed features and supervised learning, and the survival analysis is largely limited to logistic regression. We developed a weakly unsupervised conditional generative adversarial network, called pix2surv, which can be trained to estimate the time-to-event information for survival analysis directly from the chest computed tomography (CT) images of a patient. We show that the performance of pix2surv based on CT images significantly outperforms those of existing laboratory tests and image-based visual and quantitative predictors in estimating the disease progression and mortality of COVID-19 patients. Thus, pix2surv is a promising approach for performing image-based prognostic predictions. | |||||
書誌情報 |
Medical Imaging Analysis 巻 73, p. 102159-1-102159-14, 発行日 2021-07-23 |
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出版者 | ||||||
出版者 | Elsevier | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1361-8415 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1016/j.media.2021.102159 | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Unsupervised deep learning | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Survival analysis | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | COVID-19 | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Computed tomography | |||||
日本十進分類法 | ||||||
主題Scheme | NDC | |||||
主題 | 492 | |||||
著作権関連情報 | ||||||
権利情報 | Copyright (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CCBY license (http://creativecommons.org/licenses/by/4.0/) | |||||
出版タイプ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
査読の有無 | ||||||
値 | yes | |||||
連携ID | ||||||
10856 |