{"_buckets": {"deposit": "33a403b4-1d78-4a95-b269-1898b779dfa2"}, "_deposit": {"created_by": 3, "id": "7864", "owners": [3], "pid": {"revision_id": 0, "type": "depid", "value": "7864"}, "status": "published"}, "_oai": {"id": "oai:kyutech.repo.nii.ac.jp:00007864", "sets": ["9"]}, "author_link": ["34570", "402", "34562", "34567", "34571", "34573", "34569", "34565", "34563", "34568", "34564"], "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"}]}]}, "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_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_full_name_3": {"attribute_name": "著者別名", "attribute_value_mlt": [{"nameIdentifiers": [{"nameIdentifier": "34568", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "Uemura,  T."}]}, {"nameIdentifiers": [{"nameIdentifier": "34569", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "Näppi,  J.J."}]}, {"nameIdentifiers": [{"nameIdentifier": "34570", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "Watari,  C."}]}, {"nameIdentifiers": [{"nameIdentifier": "34571", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "Hironaka,  T."}]}, {"nameIdentifiers": [{"nameIdentifier": "402", "nameIdentifierScheme": "WEKO"}, {"nameIdentifier": "80295005", "nameIdentifierScheme": "e-Rad", "nameIdentifierURI": "https://nrid.nii.ac.jp/ja/nrid/1000080295005/"}, {"nameIdentifier": "55739611300", "nameIdentifierScheme": "Scopus著者ID", "nameIdentifierURI": "https://www.scopus.com/authid/detail.uri?authorId=55739611300"}, {"nameIdentifier": "25", "nameIdentifierScheme": "九工大研究者情報", "nameIdentifierURI": "https://hyokadb02.jimu.kyutech.ac.jp/html/25_ja.html"}], "names": [{"name": "Kamiya,  T."}]}, {"nameIdentifiers": [{"nameIdentifier": "34573", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "Yoshida, H."}]}]}, "item_21_publisher_7": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "Elsevier"}]}, "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": "権利", "attribute_value_mlt": [{"subitem_rights": "Copyright (c) 2021 The Authors. 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