{"created":"2023-05-15T12:00:52.764970+00:00","id":7864,"links":{},"metadata":{"_buckets":{"deposit":"54314027-2dd0-426b-aff4-07f0b005260d"},"_deposit":{"created_by":14,"id":"7864","owners":[14],"pid":{"revision_id":0,"type":"depid","value":"7864"},"status":"published"},"_oai":{"id":"oai:kyutech.repo.nii.ac.jp:00007864","sets":["8:9"]},"author_link":["34562","34563","34564","34565","402","34567"],"item_21_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2021-07-23","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"102159-14","bibliographicPageStart":"102159-1","bibliographicVolumeNumber":"73","bibliographic_titles":[{"bibliographic_title":"Medical Imaging Analysis","bibliographic_titleLang":"en"}]}]},"item_21_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"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.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_21_description_60":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"subitem_description":"Journal Article","subitem_description_type":"Other"}]},"item_21_publisher_7":{"attribute_name":"出版社","attribute_value_mlt":[{"subitem_publisher":"Elsevier","subitem_publisher_language":"en"}]},"item_21_relation_12":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1016/j.media.2021.102159","subitem_relation_type_select":"DOI"}}]},"item_21_rights_13":{"attribute_name":"著作権関連情報","attribute_value_mlt":[{"subitem_rights":"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/)"}]},"item_21_select_59":{"attribute_name":"査読の有無","attribute_value_mlt":[{"subitem_select_item":"yes"}]},"item_21_source_id_8":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1361-8423","subitem_source_identifier_type":"EISSN"},{"subitem_source_identifier":"1361-8415","subitem_source_identifier_type":"PISSN"}]},"item_21_subject_16":{"attribute_name":"日本十進分類法","attribute_value_mlt":[{"subitem_subject":"492","subitem_subject_scheme":"NDC"}]},"item_21_text_63":{"attribute_name":"連携ID","attribute_value_mlt":[{"subitem_text_value":"10856"}]},"item_21_version_type_58":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Uemura,  Tomoki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Näppi,  Janne J.","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Watari,  Chinatsu","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hironaka,  Toru","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorAffiliations":[{"affiliationNames":[{}]}],"creatorAlternatives":[{"creatorAlternative":"Kim, Hyungseop","creatorAlternativeLang":"en"},{"creatorAlternative":"金, 亨燮","creatorAlternativeLang":"ja"}],"creatorNames":[{"creatorName":"Kamiya, Tohru","creatorNameLang":"en"},{"creatorName":"神谷, 亨","creatorNameLang":"ja"},{"creatorName":"カミヤ, トオル","creatorNameLang":"ja-Kana"}],"nameIdentifiers":[{},{},{},{}]},{"creatorNames":[{"creatorName":"Yoshida,  Hiroyuki","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2023-02-02"}],"displaytype":"detail","filename":"LaSEINE-2021_027.pdf","filesize":[{"value":"3.0 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"LaSEINE-2021_027.pdf","url":"https://kyutech.repo.nii.ac.jp/record/7864/files/LaSEINE-2021_027.pdf"},"version_id":"e182ff78-9864-42ec-94fd-0285511ba60d"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Unsupervised deep learning","subitem_subject_scheme":"Other"},{"subitem_subject":"Survival analysis","subitem_subject_scheme":"Other"},{"subitem_subject":"COVID-19","subitem_subject_scheme":"Other"},{"subitem_subject":"Computed tomography","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT","subitem_title_language":"en"}]},"item_type_id":"21","owner":"14","path":["9"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2023-02-02"},"publish_date":"2023-02-02","publish_status":"0","recid":"7864","relation_version_is_last":true,"title":["Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT"],"weko_creator_id":"14","weko_shared_id":-1},"updated":"2024-04-02T08:45:38.438564+00:00"}