{"created":"2023-05-15T16:49:57.221762+00:00","id":27619,"links":{},"metadata":{"_buckets":{"deposit":"61acc80c-ae5f-485f-ab21-add771109308"},"_deposit":{"created_by":6,"id":"27619","owners":[6],"pid":{"revision_id":0,"type":"depid","value":"27619"},"status":"published"},"_oai":{"id":"oai:nagasaki-u.repo.nii.ac.jp:00027619","sets":["10:22:24"]},"author_link":["122790","122789"],"item_5_alternative_title_19":{"attribute_name":"その他のタイトル","attribute_value_mlt":[{"subitem_alternative_title":"ヒトの専門知とディープラーニングを融合した新手法MIXTUREによる、間質性肺炎の病理診断および予後予測の試み"}]},"item_5_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2022-09-16","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"1091","bibliographicPageStart":"1083","bibliographicVolumeNumber":"35","bibliographic_titles":[{"bibliographic_title":"Modern Pathology"}]}]},"item_5_date_granted_64":{"attribute_name":"学位授与年月日","attribute_value_mlt":[{"subitem_dategranted":"2022-09-16"}]},"item_5_degree_grantor_62":{"attribute_name":"学位授与機関","attribute_value_mlt":[{"subitem_degreegrantor":[{"subitem_degreegrantor_name":"Nagasaki University (長崎大学)"}],"subitem_degreegrantor_identifier":[{"subitem_degreegrantor_identifier_name":"17301","subitem_degreegrantor_identifier_scheme":"kakenhi"}]}]},"item_5_degree_name_61":{"attribute_name":"学位名","attribute_value_mlt":[{"subitem_degreename":"博士(医学)"}]},"item_5_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Interstitial pneumonia is a heterogeneous disease with a progressive course and poor prognosis, at times even worse than those in the main cancer types. Histopathological examination is crucial for its diagnosis and estimation of prognosis. However, the evaluation strongly depends on the experience of pathologists, and the reproducibility of diagnosis is low. Herein, we propose MIXTURE (huMan-In-the-loop eXplainable artificial intelligence Through the Use of REcurrent training), an original method to develop deep learning models for extracting pathologically significant findings based on an expert pathologist’s perspective with a small annotation effort. The procedure of MIXTURE consists of three steps as follows. First, we created feature extractors for tiles from whole slide images using self-supervised learning. The similar looking tiles were clustered based on the output features and then pathologists integrated the pathologically synonymous clusters. Using the integrated clusters as labeled data, deep learning models to classify the tiles into pathological findings were created by transfer-learning the feature extractors. We developed three models for different magnifications. Using these extracted findings, our model was able to predict the diagnosis of usual interstitial pneumonia, a finding suggestive of progressive disease, with high accuracy (AUC 0.90 in validation set and AUC 0.86 in test set). This high accuracy could not be achieved without the integration of findings by pathologists. The patients predicted as UIP had poorer prognosis (5-year overall survival [OS]: 55.4%) than those predicted as non-UIP (OS: 95.2%). The Cox proportional hazards model for each microscopic finding and prognosis pointed out dense fibrosis, fibroblastic foci, elastosis, and lymphocyte aggregation as independent risk factors. We suggest that MIXTURE may serve as a model approach to different diseases evaluated by medical imaging, including pathology and radiology, and be the prototype for explainable artificial intelligence that can collaborate with humans.","subitem_description_type":"Abstract"}]},"item_5_description_5":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"長崎大学学位論文 学位記番号:博(医歯薬)甲第1461号 学位授与年月日:令和4年9月16日","subitem_description_type":"Other"},{"subitem_description":"Author: Wataru Uegami, Andrey Bychkov, Mutsumi Ozasa, Kazuki Uehara, Kensuke Kataoka, Takeshi Johkoh, Yasuhiro Kondoh, Hidenori Sakanashi and Junya Fukuoka","subitem_description_type":"Other"},{"subitem_description":"Citation: Modern Pathology, 35, pp. 1083–1091; 2022","subitem_description_type":"Other"}]},"item_5_description_69":{"attribute_name":"引用","attribute_value_mlt":[{"subitem_description":"Nagasaki University (長崎大学), 博士(医学) (2022-09-16)","subitem_description_type":"Other"}]},"item_5_dissertation_number_65":{"attribute_name":"学位授与番号","attribute_value_mlt":[{"subitem_dissertationnumber":"甲医歯薬第1461号"}]},"item_5_full_name_3":{"attribute_name":"著者別名","attribute_value_mlt":[{"nameIdentifiers":[{"nameIdentifier":"122790","nameIdentifierScheme":"WEKO"}],"names":[{"name":"Uegami, Wataru"}]}]},"item_5_publisher_33":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"The United States & Canadian Academy of Pathology (USCAP)"}]},"item_5_relation_12":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1038/s41379-022-01025-7","subitem_relation_type_select":"DOI"}}]},"item_5_relation_42":{"attribute_name":"関係URI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"http://hdl.handle.net/10069/00041591","subitem_relation_type_select":"HDL"}}]},"item_5_rights_13":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/."}]},"item_5_source_id_7":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0893-3952","subitem_source_identifier_type":"ISSN"}]},"item_5_source_id_8":{"attribute_name":"EISSN","attribute_value_mlt":[{"subitem_source_identifier":"1530-0285","subitem_source_identifier_type":"ISSN"}]},"item_5_text_67":{"attribute_name":"学位の種類","attribute_value_mlt":[{"subitem_text_value":"課程博士"}]},"item_5_version_type_16":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_abf2"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"上紙, 航"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2022-09-29"}],"displaytype":"detail","filename":"ISYK1461_Uegami.pdf","filesize":[{"value":"1.9 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"ISYK1461_Uegami.pdf","objectType":"fulltext","url":"https://nagasaki-u.repo.nii.ac.jp/record/27619/files/ISYK1461_Uegami.pdf"},"version_id":"4b13f225-c3a6-4f5e-b612-d10b030c288b"}]},"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":"doctoral thesis","resourceuri":"http://purl.org/coar/resource_type/c_db06"}]},"item_title":"MIXTURE of human expertise and deep learning—developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"MIXTURE of human expertise and deep learning—developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease","subitem_title_language":"en"}]},"item_type_id":"5","owner":"6","path":["24"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2022-09-29"},"publish_date":"2022-09-29","publish_status":"0","recid":"27619","relation_version_is_last":true,"title":["MIXTURE of human expertise and deep learning—developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease"],"weko_creator_id":"6","weko_shared_id":-1},"updated":"2023-08-23T04:53:59.933891+00:00"}