Kyutacarは九州工業大学で生産された研究成果を オープンアクセスで提供する機関リポジトリシステムです。 Kyutacar is open-access repository of research by members of the Kyushu Institute of Technology.
Massachusetts General Hospital and Harvard Medical School, Kyushu Institute of Technology
Massachusetts General Hospital and Harvard Medical School
Massachusetts General Hospital and Harvard Medical School
Massachusetts General Hospital and Harvard Medical School
Kyushu Institute of Technology
Massachusetts General Hospital and Harvard Medical School
抄録
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.
雑誌名
Medical Imaging Analysis
巻
73
ページ
102159-1 - 102159-14
発行年
2021-07-23
出版者
Elsevier
ISSN
1361-8415
DOI
https://doi.org/10.1016/j.media.2021.102159
権利
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/)
Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT
LaSEINE-2021_027.pdf
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