{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00216568","sets":["1164:2735:10865:10866"]},"path":["10866"],"owner":"44499","recid":"216568","title":["畳み込みニューラルネットワークを用いた繰り返しパターンの検出手法と顕微鏡画像への応用"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-02-24"},"_buckets":{"deposit":"2a580047-5e3b-40f0-b722-c61e0625f165"},"_deposit":{"id":"216568","pid":{"type":"depid","value":"216568","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"畳み込みニューラルネットワークを用いた繰り返しパターンの検出手法と顕微鏡画像への応用","author_link":["559096","559098","559097","559095"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"畳み込みニューラルネットワークを用いた繰り返しパターンの検出手法と顕微鏡画像への応用"}]},"item_type_id":"4","publish_date":"2022-02-24","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"大阪大学大学院情報科学研究科"},{"subitem_text_value":"大阪大学大学院情報科学研究科"},{"subitem_text_value":"情報通信研究機構脳情報通信融合研究センター"},{"subitem_text_value":"大阪大学大学院情報科学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/216568/files/IPSJ-MPS22137022.pdf","label":"IPSJ-MPS22137022.pdf"},"date":[{"dateType":"Available","dateValue":"2024-02-24"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS22137022.pdf","filesize":[{"value":"2.9 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"6423a15d-d63f-4b2d-8a85-c8f289bda1dd","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"新田, 恭晟"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"瀬尾, 茂人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"細田, 一史"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松田, 秀雄"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年，顕微鏡の精度向上により顕微鏡画像処理は医学，薬学，生物学などの分野で活用が進み重要なタスクとなっている．顕微鏡画像処理では畳み込みニューラルネットワークを用いた方法が顕著な成果を出しているが物体検出ではアノテーションが必要なものが多く，労力がかかることが問題となっている．1 枚の顕微鏡画像には同一試料が複数写っていることが頻繁にある．これを利用してアノテーションを行わずとも 1 枚の画像から必要な特徴を学習できると考えられる．本研究では Deep Feature Factorization を用いて画像中に含まれる繰り返しパターンを検出する方法を提案し，顕微鏡画像へ応用し有効性を評価した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-02-24","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"22","bibliographicVolumeNumber":"2022-MPS-137"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":216568,"updated":"2025-01-19T15:48:08.801681+00:00","links":{},"created":"2025-01-19T01:17:06.686143+00:00"}