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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 |
|---|---|---|
LaSEINE-2021_027.pdf (3.0 MB)
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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 | |||||||||
| 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 | |||||||||
| 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 | en | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| 著者 |
Uemura, Tomoki
× Uemura, Tomoki× Näppi, Janne J.× Watari, Chinatsu× Hironaka, Toru× 神谷, 亨
WEKO
402
× 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. | |||||||||
| 言語 | en | |||||||||
| 書誌情報 |
en : Medical Imaging Analysis 巻 73, p. 102159-1-102159-14, 発行日 2021-07-23 |
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| 出版社 | ||||||||||
| 出版者 | Elsevier | |||||||||
| 言語 | en | |||||||||
| 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 | |||||||||
| 日本十進分類法 | ||||||||||
| 主題Scheme | NDC | |||||||||
| 主題 | 492 | |||||||||
| ISSN | ||||||||||
| 収録物識別子タイプ | EISSN | |||||||||
| 収録物識別子 | 1361-8423 | |||||||||
| ISSN | ||||||||||
| 収録物識別子タイプ | PISSN | |||||||||
| 収録物識別子 | 1361-8415 | |||||||||
| 著作権関連情報 | ||||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by/4.0/ | |||||||||
| 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/) | |||||||||
| 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 | |||||||||
| 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 | ||||||||||
| 値 | 10856 | |||||||||
