{"created":"2023-11-08T06:01:05.167260+00:00","id":2000364,"links":{},"metadata":{"_buckets":{"deposit":"8e6cd694-c310-47e7-b348-157a0b17b462"},"_deposit":{"created_by":42,"id":"2000364","owners":[42],"pid":{"revision_id":0,"type":"depid","value":"2000364"},"status":"published"},"_oai":{"id":"oai:kindai.repo.nii.ac.jp:02000364","sets":["14:2667:1697004343623"]},"author_link":[],"item_1693381679919":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"report","resourceuri":"http://purl.org/coar/resource_type/c_93fc"}]},"item_30001_bibliographic_information17":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicNumberOfPages":"9","bibliographic_titles":[{"bibliographic_title":"科学研究費助成事業研究成果報告書 (2022)","bibliographic_titleLang":"ja"}]}]},"item_30001_creator2":{"attribute_name":"研究代表者","attribute_type":"creator","attribute_value_mlt":[{"creatorAffiliations":[{"affiliationNameIdentifiers":[{"affiliationNameIdentifier":"34419","affiliationNameIdentifierScheme":"kakenhi"}],"affiliationNames":[{"affiliationName":"近畿大学","affiliationNameLang":"ja"},{"affiliationName":"Kindai University","affiliationNameLang":"en"}]}],"creatorNames":[{"creatorName":"根本, 充貴","creatorNameLang":"ja"},{"creatorName":"Nemoto, Mitsutaka","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"10451808","nameIdentifierScheme":"e-Rad"}]}]},"item_30001_description8":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"本研究では主に、教師あり機械学習の枠組みにカルテデータの病変情報を融合し、病変領域教教師ラベルのない医用画像上に高精度な教師ラベルを自動付与する手法を提案し実験的に検討を行った。提案法は、病変位置座標と病変長径のみから複数の3次元拡張U-Netを用いて病変領域教師ラベルを推定するものである。肺結節を対象とした検証実験では、Solid、GGO、Mixed-GGOの3種類の結節いずれも良好なDice係数を伴う推定教師ラベルを取得できた。また病変領域抽出に重要な画素識別の検討も行った。教師無し画素異常検知に基づくFDG-PET/CT像上の多種病変検出処理を提案し、論文にて発表した。","subitem_description_language":"ja","subitem_description_type":"Abstract"},{"subitem_description":"In this research, we mainly proposed and experimentally investigated a method for automatically assigning highly accurate teacher labels on medical images without lesion region teacher labels by fusing lesion information from medical record data into a supervised machine learning framework. The proposed method estimates lesion area teacher labels from extremely rough and weak teacher data, consisting of lesion position coordinates and lesion length diameter, by using multiple 3D U-Nets. Validation experiments by CT image data, including lung nodules, showed that the estimated teacher labels with good Dice coefficients were obtained for all three types of nodules: solid nodules, GGO nodules, and Mixed-GGO nodules. We also investigated pixel discrimination, which is essential for lesion area extraction. A multi-species lesion detection process on FDG-PET/CT images based on unsupervised pixel anomaly detection was proposed and published as a journal article.","subitem_description_language":"en","subitem_description_type":"Abstract"},{"subitem_description":"研究分野：画像診断支援","subitem_description_language":"ja","subitem_description_type":"Other"}]},"item_30001_file22":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","filename":"20K11944seika.pdf","format":"application/pdf","mimetype":"application/pdf","url":{"url":"https://kindai.repo.nii.ac.jp/record/2000364/files/20K11944seika.pdf"},"version_id":"5c06f472-8750-4755-9f5f-bd5f9eca5ac4"}]},"item_30001_funding_reference15":{"attribute_name":"助成情報","attribute_value_mlt":[{"subitem_award_numbers":{"subitem_award_number":"20K11944","subitem_award_uri":"https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-20K11944/"},"subitem_award_titles":[{"subitem_award_title":"カルテ情報を用いた半教師あり学習に基づく医用画像上病変検出支援システムの開発","subitem_award_title_language":"ja"},{"subitem_award_title":"Computer-aided detection development based on semi-supervised learning with medical chart information","subitem_award_title_language":"en"}],"subitem_funder_names":[{"subitem_funder_name":"独立行政法人日本学術振興会","subitem_funder_name_language":"ja"},{"subitem_funder_name":"Japan Society for the Promotion of Science","subitem_funder_name_language":"en"}]}]},"item_30001_language10":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_30001_subject7":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"コンピュータ支援診断AI","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"弱教師学習","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"医用画像","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"3D U-Net","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"適応的閾値処理","subitem_subject_language":"ja","subitem_subject_scheme":"Other"}]},"item_30001_title0":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"カルテ情報を用いた半教師あり学習に基づく医用画像上病変検出支援システムの開発","subitem_title_language":"ja"},{"subitem_title":"Computer-aided detection development based on semi-supervised learning with medical chart information","subitem_title_language":"en"}]},"item_30001_version_type12":{"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_title":"カルテ情報を用いた半教師あり学習に基づく医用画像上病変検出支援システムの開発","item_type_id":"40001","owner":"42","path":["1697004343623"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2023-11-08"},"publish_date":"2023-11-08","publish_status":"0","recid":"2000364","relation_version_is_last":true,"title":["カルテ情報を用いた半教師あり学習に基づく医用画像上病変検出支援システムの開発"],"weko_creator_id":"42","weko_shared_id":-1},"updated":"2024-02-09T05:15:00.306064+00:00"}