{"created":"2025-01-19T01:03:07.575093+00:00","updated":"2025-01-19T21:44:46.149708+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00199001","sets":["1164:4619:9659:9887"]},"path":["9887"],"owner":"44499","recid":"199001","title":["CNN特徴量の解析と特徴選択-超拡大大腸内視鏡画像を用いた腫瘍性病変認識に向けて‐"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-08-28"},"_buckets":{"deposit":"9cab6b65-9168-4710-963b-0597925c9edf"},"_deposit":{"id":"199001","pid":{"type":"depid","value":"199001","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"CNN特徴量の解析と特徴選択-超拡大大腸内視鏡画像を用いた腫瘍性病変認識に向けて‐","author_link":["481135","481140","481142","481138","481141","481134","481136","481143","481137","481145","481139","481144"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"CNN特徴量の解析と特徴選択-超拡大大腸内視鏡画像を用いた腫瘍性病変認識に向けて‐"},{"subitem_title":"Analysis and Feature Selection of CNN Features -Recognition of Neoplasia by using Endocytoscopic Images-","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"セッション6","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2019-08-28","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"名古屋大学大学院情報学研究科"},{"subitem_text_value":"昭和大学横浜市北部病院消化器センター"},{"subitem_text_value":"昭和大学横浜市北部病院消化器センター"},{"subitem_text_value":"名古屋大学大学院情報学研究科"},{"subitem_text_value":"昭和大学横浜市北部病院消化器センター"},{"subitem_text_value":"名古屋大学大学院情報学研究科／名古屋大学情報基盤センター"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate school of Informatics, Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Digestive Disease Center, Showa University Northern Yokohama Hospital","subitem_text_language":"en"},{"subitem_text_value":"Digestive Disease Center, Showa University Northern Yokohama Hospital","subitem_text_language":"en"},{"subitem_text_value":"Graduate school of Informatics, Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Digestive Disease Center, Showa University Northern Yokohama Hospital","subitem_text_language":"en"},{"subitem_text_value":"Graduate school of Informatics, Nagoya University / Information Technology Center, Nagoya 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":44499,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/199001/files/IPSJ-CVIM19218025.pdf","label":"IPSJ-CVIM19218025.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM19218025.pdf","filesize":[{"value":"2.3 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"f8c9a9fd-e4d4-47b4-97f8-f292d983142c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"伊東, 隼人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"森, 悠一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"三澤, 将史"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小田, 昌宏"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"工藤, 進英"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"森, 健策"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hayato, Itoh","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuichi, Mori","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masashi, Misawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masahiro, Oda","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shin-ei, Kudo","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kensaku, Mori","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11131797","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":"2188-8701","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"ポリープの病理学的パターンの識別はポリープ表面を超高倍率拡大観察して得られるテクスチャのパターンに基づく．深層学習は大規模データに基づく表現学習方法として様々な分野で用いられており，医用画像における病理学的パターンの識別も応用先のひとつである．深層学習は与えられた学習データに対して損失関数を最小化する特徴量表現を達成する．しかし，これは与えられた深層学習のアーキテクチャと損失関数に対する最尤推定の意味での最適化であり，識別的な特徴量表現が達成できているかどうかは確かでない．本稿ではポリープ表面の超拡大大腸内視鏡画像を対象に深層学習を用いて特徴量抽出を行い，得られた特徴量とテクスチャ特徴量の比較を行うことで，深層学習によって得られる特徴量の実験的な解析を行う．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Pathological pattern classification is based on texture patterns in ultra magnified view of polyp surfaces. Deep learning is known as an useful representation learning method with large dataset in several fields including pathological classification of medical images. This representation learning method achieves an optimal representation of patterns for predefined architecture by minimising a value of loss function. However, this is the optimisation in the meaning of maximum likelihood estimation with train data for the given architecture and loss function. Therefore, whether the extracted feature is really discriminative feature or not is unclear. In this work, we analyse discriminative and generalisation ability of deep-learning based feature by comparing with texture future for colorectal endocytoscopic images of polyp surfaces.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータビジョンとイメージメディア（CVIM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2019-08-28","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"25","bibliographicVolumeNumber":"2019-CVIM-218"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":199001,"links":{}}